{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fc06efc0",
   "metadata": {},
   "source": [
    "# High-dimensional Analyzers\n",
    "\n",
    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/brainpy/blob/master/docs_version2/tutorial_analysis/highdim_analysis.ipynb)\n",
    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/brainpy/brainpy/blob/master/docs_version2/tutorial_analysis/highdim_analysis.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "068c7d67",
   "metadata": {},
   "source": [
    "@[Chaoming Wang](https://github.com/chaoming0625)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83374185",
   "metadata": {},
   "source": [
    "It's hard to analyze high-dimensional systems. However, we have to analyze high-dimensional systems."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b102eccd",
   "metadata": {},
   "source": [
    "Here, based on numerical optimization methods, BrainPy provides [brainpy.analysis.SlowPointFinder](../apis/auto/analysis/generated/brainpy.analysis.highdim.SlowPointFinder.rst) to help users find **slow points** (or **fixed points**) [1] for your high-dimensional dynamical systems."
   ]
  },
  {
   "cell_type": "code",
   "id": "07a97a76",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:34:29.344310Z",
     "start_time": "2025-10-06T03:34:23.946943Z"
    }
   },
   "source": [
    "import brainpy as bp\n",
    "import brainpy.math as bm\n",
    "\n",
    "bm.set_platform('cpu')\n",
    "\n",
    "bp.__version__"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.0.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "id": "c95effe7",
   "metadata": {},
   "source": [
    "## What are slow points?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f0d5511",
   "metadata": {},
   "source": [
    "For the given system,\n",
    "\n",
    "$$\n",
    "\\dot{x} = f(x),\n",
    "$$\n",
    "\n",
    "we wish to find values $x^∗$ around which the system is approximately linear. Using Taylor series expansion, we have\n",
    "\n",
    "$$\n",
    "f(x^* + \\delta x) = f(x^*) + f'(x^*)\\delta x + 1/2 \\delta x f''(x^*) \\delta x + \\cdots\n",
    "$$\n",
    "\n",
    "We want the first derivative term (i.e., the linear term) to be dominant, which means $f(x^*) = 0$ or $f(x^*) \\approx 0$. \n",
    "\n",
    "- For $f(x^*) \\approx 0$ which is nonzero but small, we call the point $x^*$ a **slow point**. \n",
    "- More specially, if $f(x^*) = 0$, $x^*$ is a **fixed point**. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feb0f208",
   "metadata": {},
   "source": [
    "## How to find slow points?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad0c6854",
   "metadata": {},
   "source": [
    "In order to find slow points, we can first define an auxiliary scalar function for your continous system $\\dot{x} = f(x)$, \n",
    "\n",
    "$$\n",
    "p(x) = |f(x)|^2.\n",
    "$$\n",
    "\n",
    "Or, if your system is discrete $x_n = f(x_{n-1})$, the auxiliary scalar function can be defined as\n",
    "\n",
    "$$\n",
    "p(x) = |x - f(x)|^2.\n",
    "$$\n",
    "\n",
    "If $x^*$ is a slow point, $p(x^*) \\to 0$. \n",
    "\n",
    "Then, by minimizing the scalar function $p(x)$, we can get the candidate points for slow points and for further linearization. For the linear system, it's stability is evaluated by the eigenvalues of Jacobian matrix. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b15ec21",
   "metadata": {},
   "source": [
    "Here, BrainPy provides [brainpy.analysis.SlowPointFinder](../apis/auto/analysis/generated/brainpy.analysis.highdim.SlowPointFinder.rst). It receives ``f_cell`` to specify the target function/object to analyze.\n",
    "\n",
    "If the provided ``f_cell`` is a function, ``SlowPointFinder`` can supports to specify:\n",
    "\n",
    "- ``f_type``: the type of the function (it can be \"continuous\" or \"discrete\").\n",
    "- ``f_loss``: the loss function to minimize the optimization error.\n",
    "- ``args``: extra arguments passed into the function when performing fixed point optimization.\n",
    "\n",
    "If the provided ``f_cell`` is an instance of ``DynamicalSystem``, ``SlowPointFinder`` can supports to specify:\n",
    "\n",
    "- ``f_loss``: the loss function to minimize the optimization error.\n",
    "- ``args``: extra arguments passed into the defined ``update()`` function when performing fixed point optimization.\n",
    "- ``inputs`` and ``fun_inputs``: inputs to this dynamical system. Similar to the inputs of ``DSRunner`` and ``DSTrainer``.\n",
    "- ``target_vars``: the selected variables which are used to optimize fixed points. Other variables like \"input\" and \"spike\" can be ignored."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Then, ``brainpy.analysis.SlowPointFinder`` can help you:\n",
    "\n",
    "- optimize to find the fixed/slow points with gradient descent algorithms (``find_fps_with_gd_method()``) or nonlinear optimization solver (``find_fps_with_opt_solver()``)\n",
    "- exclude any fixed points whose losses are above threshold: ``filter_loss()``\n",
    "- exclude any non-unique fixed points according to a tolerance: ``keep_unique()``\n",
    "- exclude any far-away \"outlier\" fixed points: ``exclude_outliers()``\n",
    "- computing the jacobian matrix for the given fixed/slow points: ``compute_jacobians()``"
   ],
   "id": "deb8cdbfe7e2ec4"
  },
  {
   "cell_type": "markdown",
   "id": "22297b70",
   "metadata": {},
   "source": [
    "## Example 1: Decision Making Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a9f293",
   "metadata": {},
   "source": [
    "``brainpy.analysis.SlowPointFinder`` is aimed to find slow/fixed points of high-dimensional systems. Of course, it can optimize to find fixed points of low-dimensional systems. We take the 2D decision-making system as an example."
   ]
  },
  {
   "cell_type": "code",
   "id": "423e768f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:34:29.488905Z",
     "start_time": "2025-10-06T03:34:29.484537Z"
    }
   },
   "source": [
    "# parameters\n",
    "\n",
    "gamma = 0.641  # Saturation factor for gating variable\n",
    "tau = 0.06  # Synaptic time constant [sec]\n",
    "a = 270.\n",
    "b = 108.\n",
    "d = 0.154\n",
    "\n",
    "JE = 0.3725  # self-coupling strength [nA]\n",
    "JI = -0.1137  # cross-coupling strength [nA]\n",
    "JAext = 0.00117  # Stimulus input strength [nA]\n",
    "\n",
    "mu = 20.  # Stimulus firing rate [spikes/sec]\n",
    "coh = 0.5  # Stimulus coherence [%]\n",
    "Ib1 = 0.3297\n",
    "Ib2 = 0.3297"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "id": "eb220677",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:34:29.549517Z",
     "start_time": "2025-10-06T03:34:29.541954Z"
    }
   },
   "source": [
    "@bp.odeint\n",
    "def int_s1(s1, t, s2, coh=0.5, mu=20.):\n",
    "  I1 = JE * s1 + JI * s2 + Ib1 + JAext * mu * (1. + coh)\n",
    "  r1 = (a * I1 - b) / (1. - bm.exp(-d * (a * I1 - b)))\n",
    "  return - s1 / tau + (1. - s1) * gamma * r1\n",
    "\n",
    "@bp.odeint\n",
    "def int_s2(s2, t, s1, coh=0.5, mu=20.):\n",
    "  I2 = JE * s2 + JI * s1 + Ib2 + JAext * mu * (1. - coh)\n",
    "  r2 = (a * I2 - b) / (1. - bm.exp(-d * (a * I2 - b)))\n",
    "  return - s2 / tau + (1. - s2) * gamma * r2\n",
    "\n",
    "def step(s):\n",
    "    ds1 = int_s1.f(s[0], 0., s[1])\n",
    "    ds2 = int_s2.f(s[1], 0., s[0])\n",
    "    return bm.asarray([ds1, ds2])"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "id": "46266662",
   "metadata": {},
   "source": [
    "We first use [brainpy.analysis.PhasePlane2D](./lowdim_analysis.ipynb) to get the standard answer. "
   ]
  },
  {
   "cell_type": "code",
   "id": "6b9a2473",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:34:33.317891Z",
     "start_time": "2025-10-06T03:34:29.584504Z"
    }
   },
   "source": [
    "analyzer = bp.analysis.PhasePlane2D(\n",
    "    model=[int_s1, int_s2],\n",
    "    target_vars={'s1': [0, 1], 's2': [0, 1]},\n",
    "    resolutions=0.001,\n",
    ")\n",
    "analyzer.plot_fixed_point(select_candidates='aux_rank', with_plot=False)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I am searching fixed points ...\n",
      "I am filtering out fixed point candidates with auxiliary function ...\n",
      "I am trying to find fixed points by optimization ...\n",
      "\tThere are 100 candidates\n",
      "I am trying to filter out duplicate fixed points ...\n",
      "\tFound 3 fixed points.\n",
      "\t#1 s1=0.2827633321285248, s2=0.40635180473327637 is a saddle node.\n",
      "\t#2 s1=0.013946513645350933, s2=0.6573889851570129 is a stable node.\n",
      "\t#3 s1=0.7004518508911133, s2=0.004864312242716551 is a stable node.\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "id": "4d497cac",
   "metadata": {},
   "source": [
    "Then, let's check whether the high-dimensional analyzer also works. "
   ]
  },
  {
   "cell_type": "code",
   "id": "75ddba03",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:34:39.532172Z",
     "start_time": "2025-10-06T03:34:36.662280Z"
    }
   },
   "source": [
    "finder = bp.analysis.SlowPointFinder(f_cell=step, f_type=\"continuous\")\n",
    "finder.find_fps_with_gd_method(\n",
    "    candidates=bm.random.random((1000, 2)),\n",
    "    tolerance=1e-5,\n",
    "    num_batch=200,\n",
    "    optimizer=bp.optimizers.Adam(bp.optimizers.ExponentialDecay(0.01, 1, 0.9999))\n",
    ")\n",
    "finder.filter_loss(1e-5)\n",
    "finder.keep_unique()"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\codes\\projects\\BrainPy\\brainpy\\version2\\optim\\scheduler.py:355: UserWarning: ExponentialDecay is abandoned, please use ExponentialDecayLR insteadly.\n",
      "  warnings.warn(\"ExponentialDecay is abandoned, please use ExponentialDecayLR insteadly.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizing with Adam(lr=ExponentialDecay(0.01, decay_steps=1, decay_rate=0.9999), last_call=-1), beta1=0.9, beta2=0.999, eps=1e-08) to find fixed points:\n",
      "    Batches 1-200 in 0.19 sec, Training loss 0.0624059550\n",
      "    Batches 201-400 in 0.15 sec, Training loss 0.0052427738\n",
      "    Batches 401-600 in 0.12 sec, Training loss 0.0007515235\n",
      "    Batches 601-800 in 0.11 sec, Training loss 0.0001555069\n",
      "    Batches 801-1000 in 0.11 sec, Training loss 0.0000349458\n",
      "    Batches 1001-1200 in 0.11 sec, Training loss 0.0000083204\n",
      "    Stop optimization as mean training loss 0.0000083204 is below tolerance 0.0000100000.\n",
      "Excluding fixed points with squared speed above tolerance 1e-05:\n",
      "    Kept 954/1000 fixed points with tolerance under 1e-05.\n",
      "Excluding non-unique fixed points:\n",
      "    Kept 3/954 unique fixed points with uniqueness tolerance 0.025.\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "id": "5a9d31ff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:34:39.552198Z",
     "start_time": "2025-10-06T03:34:39.546610Z"
    }
   },
   "source": [
    "finder.fixed_points"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.70045185, 0.00486432],\n",
       "       [0.2827629 , 0.4063515 ],\n",
       "       [0.01394648, 0.657389  ]], dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "id": "a43baa44",
   "metadata": {},
   "source": [
    "Yeah, the fixed points found by ``brainpy.analysis.PhasePlane2D`` and ``brainpy.analysis.SlowPointFinder`` are nearly the same. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd0a2308",
   "metadata": {},
   "source": [
    "## Example 2: Continuous-attractor Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52c78c8c",
   "metadata": {},
   "source": [
    "Continuous-attractor neural network [2] proposed by Si Wu is a special model which has a line of attractors. "
   ]
  },
  {
   "cell_type": "code",
   "id": "ce20c839",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:34:39.585210Z",
     "start_time": "2025-10-06T03:34:39.575509Z"
    }
   },
   "source": [
    "class CANN1D(bp.NeuGroup):\n",
    "  def __init__(self, num, tau=1., k=8.1, a=0.5, A=10., J0=4., z_min=-bm.pi, z_max=bm.pi):\n",
    "    super(CANN1D, self).__init__(size=num)\n",
    "\n",
    "    # parameters\n",
    "    self.tau = tau  # The synaptic time constant\n",
    "    self.k = k  # Degree of the rescaled inhibition\n",
    "    self.a = a  # Half-width of the range of excitatory connections\n",
    "    self.A = A  # Magnitude of the external input\n",
    "    self.J0 = J0  # maximum connection value\n",
    "\n",
    "    # feature space\n",
    "    self.z_min = z_min\n",
    "    self.z_max = z_max\n",
    "    self.z_range = z_max - z_min\n",
    "    self.x = bm.linspace(z_min, z_max, num)  # The encoded feature values\n",
    "    self.rho = num / self.z_range  # The neural density\n",
    "    self.dx = self.z_range / num  # The stimulus density\n",
    "\n",
    "    # variables\n",
    "    self.u = bm.Variable(bm.zeros(num))\n",
    "    self.input = bm.Variable(bm.zeros(num))\n",
    "\n",
    "    # The connection matrix\n",
    "    self.conn_mat = self.make_conn(self.x)\n",
    "\n",
    "    # function\n",
    "    self.integral = bp.odeint(self.derivative)\n",
    "\n",
    "  def derivative(self, u, t, Iext):\n",
    "    r1 = bm.square(u)\n",
    "    r2 = 1.0 + self.k * bm.sum(r1)\n",
    "    r = r1 / r2\n",
    "    Irec = bm.dot(self.conn_mat, r)\n",
    "    du = (-u + Irec + Iext) / self.tau\n",
    "    return du\n",
    "\n",
    "  def dist(self, d):\n",
    "    d = bm.remainder(d, self.z_range)\n",
    "    d = bm.where(d > 0.5 * self.z_range, d - self.z_range, d)\n",
    "    return d\n",
    "\n",
    "  def make_conn(self, x):\n",
    "    assert bm.ndim(x) == 1\n",
    "    x_left = bm.reshape(x, (-1, 1))\n",
    "    x_right = bm.repeat(x.reshape((1, -1)), len(x), axis=0)\n",
    "    d = self.dist(x_left - x_right)\n",
    "    Jxx = self.J0 * bm.exp(-0.5 * bm.square(d / self.a)) / (bm.sqrt(2 * bm.pi) * self.a)\n",
    "    return Jxx\n",
    "\n",
    "  def get_stimulus_by_pos(self, pos):\n",
    "    return self.A * bm.exp(-0.25 * bm.square(self.dist(self.x - pos) / self.a))\n",
    "\n",
    "  def update(self, tdi):\n",
    "    self.u.value = self.integral(self.u, tdi.t, self.input, tdi.dt)\n",
    "    self.input[:] = 0.\n",
    "\n",
    "  def cell(self, u):\n",
    "    return self.derivative(u, 0., 0.)"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:34:40.150553Z",
     "start_time": "2025-10-06T03:34:39.598237Z"
    }
   },
   "source": [
    "cann = CANN1D(num=512, k=0.1, A=30)"
   ],
   "id": "afe25da2431c8440",
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The following code demonstrates how to use ``SlowPointFinder`` to find fixed points of a continuous attractor neural network."
   ],
   "id": "68c87ca2b32dc8ea"
  },
  {
   "cell_type": "code",
   "id": "24737b1e",
   "metadata": {
    "scrolled": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:36:32.530332Z",
     "start_time": "2025-10-06T03:34:40.237910Z"
    }
   },
   "source": [
    "# initialize an instance of slow point finder\n",
    "finder = bp.analysis.SlowPointFinder(\n",
    "    f_cell=cann,\n",
    "    target_vars={'u': cann.u},\n",
    "    dt=1.,\n",
    ")\n",
    "\n",
    "# we can initialize our candidate points with noisy bumps.\n",
    "candidates = cann.get_stimulus_by_pos(bm.arange(-bm.pi, bm.pi, 0.01).reshape((-1, 1)))\n",
    "candidates += bm.random.normal(0., 0.01, candidates.shape)\n",
    "\n",
    "# optimize to find fixed points\n",
    "finder.find_fps_with_opt_solver({'u': candidates})\n",
    "finder.filter_loss(1e-6)\n",
    "finder.keep_unique()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimizing with BFGS to find fixed points:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\codes\\projects\\BrainPy\\brainpy\\version2\\dynsys.py:325: UserWarning: \n",
      "From brainpy>=2.4.3, update() function no longer needs to receive a global shared argument.\n",
      "\n",
      "Instead of using:\n",
      "\n",
      "  def update(self, tdi, *args, **kwagrs):\n",
      "     t = tdi['t']\n",
      "     ...\n",
      "\n",
      "Please use:\n",
      "\n",
      "  def update(self, *args, **kwagrs):\n",
      "     t = bp.share['t']\n",
      "     ...\n",
      "\n",
      "  warnings.warn(_update_deprecate_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Found 629 fixed points from 629 initial points.\n",
      "Excluding fixed points with squared speed above tolerance 1e-06:\n",
      "    Kept 357/629 fixed points with tolerance under 1e-06.\n",
      "Excluding non-unique fixed points:\n",
      "    Kept 357/357 unique fixed points with uniqueness tolerance 0.025.\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The found fixed points are a series of attractor. We can visualize this line of attractors on a 2D space."
   ],
   "id": "82fac9a52c3d5a2a"
  },
  {
   "cell_type": "code",
   "id": "032c9bb8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:36:32.927345Z",
     "start_time": "2025-10-06T03:36:32.653283Z"
    }
   },
   "source": [
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pca = PCA(2)\n",
    "fp_pcs = pca.fit_transform(finder.fixed_points['u'])\n",
    "plt.plot(fp_pcs[:, 0], fp_pcs[:, 1], 'x', label='fixed points')\n",
    "plt.xlabel('PC 1')\n",
    "plt.ylabel('PC 2')\n",
    "plt.title('Fixed points PCA')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "These fixed points can also be plotted on the feature space. In the following, we plot the selected points."
   ],
   "id": "19dc9c7d080f72c"
  },
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:36:32.936131Z",
     "start_time": "2025-10-06T03:36:32.931713Z"
    }
   },
   "source": [
    "def visualize_fixed_points(fps, plot_ids=(0,), xs=None):\n",
    "  for i in plot_ids:\n",
    "    if xs is None:\n",
    "      plt.plot(fps[i], label=f'FP-{i}')\n",
    "    else:\n",
    "      plt.plot(xs, fps[i], label=f'FP-{i}')\n",
    "  plt.legend()\n",
    "  plt.xlabel('Feature')\n",
    "  plt.ylabel('Bump activity')\n",
    "  plt.show()"
   ],
   "id": "2d5df92c4fc6d3c",
   "outputs": [],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "id": "bad2cab4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:36:33.044794Z",
     "start_time": "2025-10-06T03:36:32.942142Z"
    }
   },
   "source": [
    "visualize_fixed_points(finder.fixed_points['u'],\n",
    "                       plot_ids=(10, 20, 30, 40, 50, 60, 70, 80),\n",
    "                       xs=cann.x)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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L99gxvF4vb7+tCZ5F85diDadSf7CHqjHZFYCV9/wNjrR0+lqb2fa//yPEnFfmYylwoXpD9I8pMQFMnTo1+onpT3/6kywxSSSSSx4pZCQXPKqq8t3XDhEMq6yemc1n5+WJN3h/HYz0QlY5XPmIEHrp2EtU9VWRak3l/y79v0JsaNMmBt56CwwG8n74A3525wIUBf64t5mP9rWx+616AFZ8fir2236qZXnaD+Df+ms++C8tw7Loptv4m888wtS0qbh9bv5177/S91o1hFRsM9NJv3sVruuug2CQtu89yoYNGxgaGiIjI4Prb/oMi24oBmDL76vwDY+KDpvTyer/o5mWd7/xCt1No9kVxaCQ9rmpYFTwVvbhPSwOsVqzZg0ul4ve3l4+/vjj033ZJRKJ5KJAChnJBc9rFS3srOvFZjaw7rMxvpjm3bD3t9rXN/4LGM3RUNdwF09XPA3A3172t6TZ0qIx1e+n48c/ASD9r/4K++xyFhalc+cirez07ovHCAXDFMxMY9qSbHBlw6rvAbDjD88x2NNFSlY2y+64C7PBHBVJvbsb8dcNoJgNpH62FEVRyPnedzE4nbQ1NbF71y7tUm+8EZPJxGXXF5GWk4R3KMCuiHDSKVu0lLLFy1HDYTb+9j+FzIs524HrqnwA+t+qQw2MLpu02WysWaPNufjoo4/o7+8/5ddcIpFILhakkJFc0Iz4Q/z07UoAHlg1lfy0MXuFVBXe+Q6gwry/gKLLhfs+te8pPAEPszNmc/tUcclZ30sv4W9owJiRwaQHvhn9979fM50ZipnsIRUUuPLOaaO7TRZ+hYGUuezpnATANV/+GmarZmJbmL2QW4tu4cudnwXAtaoAU5oWM2VmkvH1v6FiwXxUoHzGDKZMmQKA0WhgxeenAnDwg2b6O4aF67z6r+7FaDLRcGAfNXt2CjHXygKMyRZCvV4GtzQLsfLycoqKiggGg7z77rsnfJ0lEonkYkUKGckFzXNb62gf8DI51c7/ubJEDB77MzTvBHMSrF4nhGr6a3i95nUAvr302xiU0R/1kNtN99P/DkDmAw9gHDPCOz3JwmcVTSwddahY062jJzWa+Ni/gpBqID/JTWlxhvCY3wj8JVnBdDpNvewpqhZivStW0JmdjSEU4rK2NiFWVJ5B0ewMwmGVra+I90vNzmHhjbcCsPmF/yIcGl1WZ7AYSblBe00GNzYTGvJHY4qisHbtWhRF4ciRI7S0tCCRSCSXIlLISC5Y+jx+ntlYA8Aj103DahoznjsUhA3f175e9g1wiRten9r7FGE1zLWF1zIvc54Q6/mv5wi53VinlpH6uTuEWNWeDtReP34FNhi8PL+1PhrrrK/lyN5DAFydVYvy4Y+isfBwgPDH2ibr32a+wb8eeIpQWBMd4XCYDRs3AlBWVU3wv18gGDO07vI7ylAUqNvfTUf9gBBbcusXsLmS6Wtr4ciWD4WYfV4m5slOVH8oajDWycnJYe7cuQCyHVsikVyySCEjuWD59ZZaBr1BZuYmc+v8yWLwwO+h+zjY02HFt8RQ1wE+aPoAg2LgWwvEWKi/n74XXgBg0re+hTJmoqUaVtn9lmaqTV+YwYgBfrmxBveIZsLd8crvAZi+YC45do/W7t1aAcDgx62o3hCGbBu7M49R467hzdo3ATh69CgdHR1YrVbmh4KEh4fpffZZ4brScx1MW6qJsZ1v1Aoxa1ISSz6rCa5t//s/hIKjpmBFUUi5TmvHHtrWSmhMOzZoLZMGg4Ha2lrq6uoSvs4SiURyMSOFjOSCxD0S4L+3aaLiodVjlkEChEOw5V+0r694UJgZA/AfB/8DgJum3MSU1ClCrPe/XyDs8WCdPh3XtdcKsZp9XfS1ebDYTXz+L2YxLdvJgDfIf31UR09zI8d3bgVg2Re/BnM+p91p888Je4MMfdwKQOrqYr4696vR6wiGgmzZskW737JlFHzjG9p1vPg7gt3dwuMvvrEYxaDQeLiXtshuJp35a27EkZrGQFcHBz94T4hZp6VhKU6GoMrAxiYhlpaWxsKFWjv6hx+K2RyJRCK5FJBCRnJB8t/b6hnyBZme7WL1zJilZYdegd5aLRuz6F4hVNVXxcamjSgo/J85/0eIhQYH6f2t1uE06b6voxhGf/xVVWX32/UAzF2ZT5LDzLeu1Uy4z2+t56M//h5UlbLFy5lUWAxX/T2gwLE3GXp/P6o3iCnTjr18EndOv5MUawoNAw28vPVl2tvbMZvNLF26FOc112CbNxd1ZITe3/xGuL6UzCRmLteyMnvWi8PszFYbS279PAC733xF8MooikLyai0r49nVIXhlAK688koMBgONjY3RceISiURyqSCFjOSCY9gf5L8+rgfgGytLY7Ix4dFszPJvgNUp3Pe/Dv0XAKuLVlOSIpqD+154gfDgIJayUm22yxjqD/bQ0zyE2Wpk3rVaC/ba2bmUTHLAQDfV2zYDsOz2O7U7ZE6HmTcTVq0MbdfmuLhWFqAYFBxmB3858y9Bhb3b9wKwePFikpKSUBSFSX/zN9r1vPR7QkMe4ToWXFcECjQc7KG3VYzNWXkdNlcy7o52qiLZIR1raQrmfCcEwwxtE83EycnJ0e2zenZIIpFc2txzzz0oihJ3VFdXCzGLxUJZWRmPPfYYwWBw3PP95Cc/YfHixbhcLrKysrj11luprKwUbuP1ern//vvJyMjA6XRyxx130NHRMc4ZzxxSyEguOF7a2USvx09hehI3zskVg1XvQtcxsCbD4r8WQi1DLdE1AffOFjM14eFhep/XMiCTvn5ffDYmMsNlzjWTsTm0WTRGg8J9V5eysH8vqCpF8xaSPaVs9KRX/R2e0BrCQTvGFCNJ87Kiob+Y8RcUBAtweBwYjAZh+6vzmmuwlJQQHhzE/cfRxZAAqdlJTJmfCcC+98XsidlmY8GaGwHY9fofhbkyiqLgulqbK+PZ1krYHxLuu2LFChRFoaqqivb2diQSyaXP9ddfT1tbm3CUlJQIsaqqKh555BHWrVvHz3/+83HPtWnTJu6//362b9/Oe++9RyAQ4LrrrsPjGf3A9dBDD/HGG2/w8ssvs2nTJlpbW7n99tvHPeeZQgoZyQWFPxjm15s1s+vXry4V9ykBbNfapln4ZbCnCqHnDz1PSA2xLHcZ5ZPKhZj79TcIud2YCwtJXnu9EGuvcdNZP4DRbGDetYVC7DMlScz0HAfAN3uVEFOz5jKk3AWAK/sAinE0c5RiTWG5VxMvg5MGcblc0ZhiMJD+lXsA6PnNb1Bj1ggs+Ix2Dcd3tOPpF82789fchMlsoaO2mqbDB4WYvXwSxgwb4eEgnl2iWMnIyGDWLG2Y4LZt25BIJJc+VquVnJwc4dAX1OqxoqIi7rvvPlavXs3rr78+7rneeecd7rnnHsrLy5k3bx7PP/88jY2N7NmzBwC3282zzz7L448/zqpVq1i4cCHPPfccW7duZfv27Wf1eUohI7mgePtQG+0DXjJdVu5YGNOp1HEE6jaBYoAlXxNCbp+bV6tfBYjzxqiqSu8L/w1A+l/ejWI0CvEDH2pty9OXZJOUbBFixza+i0EN02bN5qUGo5AFGTnSQyiQjIF+HO0/B99QNNbV1UWoO4SKyhbzFmr6a4TzptxyC8aMDIKtbQysFwfW5UxJIbcshXBI5cCHonk3KTmF8siW7V1v/FGIKQYF15VaVmZoSwtqKCzE9azQoUOHGBoaQiKRnAaqCn7P+TnG/P4509jtdvx+/4lvGMHtdgOQnp4OwJ49ewgEAqxevTp6mxkzZlBYWHjWPzyZTnwTieTc8dtIp9LdSwvFuTEAO57R/pxxE6SKmZPXql/DF/IxPW06S3KWCLHhbdvwV9dgSEoiJbIdWmeoz0vNvi4A5qwsEGKhYID972ulqiOpcznS4mZvYx8Li7T/uENbtU4lh3MHir8b9v8PLNHKXbsiqwiCGUE8Zg8vHn2RR5ePrrs3WK2k3f1Fup/6N3r+61mSb7xhdIIwWlamrfoghza3snBtMRbb6H/VRTfeyoH33qa+Yg/dTQ1MKiiKxhwLsxh4v4FQv4+Rwz0kzc2MxvLz88nLy6O1tZW9e/dy1VVXIZFITpHAMPw478S3Oxv8YytYHCd98zfffBPnmIGfa9eu5eWXXxZuo6oqGzZsYP369TzwwAMndd5wOMyDDz7IihUrmD17NgDt7e1YLBZSU1OF22ZnZ5/1crbMyEguGA61uNnT0IfJoPDFJaJQwdMDB/6gfb3sG0IorIb5faU24+WuGXcJggC0lmuAlNtuwzimxANwaFMLalglb2oqk/JF43DVjq14+npxpKYx54orAXguYkIOtHvw17nBAI4rIqbinb+GcBifz0dFRQUAK5atAOCNmjdw+9zC+dP+4i9QbDZ8R44ysm+fECueM4nU7CT8I0GO7xTNcqk5uZQuWgrA/vfeFmKK2Yhjidb55Nkumn4Bli7V7rdr1y5CoVBcXCKRXDqsXLmSioqK6PHUU09FY7rIsdlsrF27ljvvvJN169axZcsWnE5n9HjxxRfjznv//fdz6NAhXnrppXP5dMZFZmQkFwz63Ji1c3LJSraJwT3PQdALufOgcJkQ+rjlY5oGm3CZXdxQcoMQ8zc2MhSZqpt2991CLBgIcfgjLasyd1V+3PXse0cbaDd39fVctaKM3+9t4+1D7bS5R7BFsjH28kmYls2Drf+kDeir/ZD9vS78fr+24Xrh9fxX239R2VfJH6v+yFdnfzV6flNaGsk33ID7lVfo//3vSbrssmhMMSiUX5nHx/9bzeEtLZRfmScItHnX3UD1rm0c2fwBV33xHsy20dfLsSSXwQ+b8NW6CXQOY84a3U9VXl7Ou+++y+DgIEePHo1+mpJIJCeJOUnLjJyvxz4FHA4HZWVlCWMrV67kmWeewWKxkJeXhykyHHTRokXRD2KgZVTG8s1vfpM333yTzZs3k58/+nszJycHv99Pf3+/kJXp6OggJ0ecvH6mkRkZyQVB/7Cf1yq0fUBfXl4kBsMh2P2c9vXS+yAm4/JSpfap4JayW0iK+Y/e9+LvQFVxXHUl1iliO3bVrk68QwGc6VZK5k4SYh211bQeP4rBaGTu6rXMyktmSUk6obDK77fUMbyvEwDn8lywJcOCvwRA3f5Ldu7UljsuWbIEg8HA3TM1AfXSsZeiawt00u78AgADb79DKGZL9YzluRhNBrqbhuisHxRiRbPnkZqdi39kmKMfbxJiplQrthla+cuzQ8zKmEwmFi1aBBC9TolEcgooilbeOR9HzO++T4IucgoLC6MiBjSvTFlZWfTQGxVUVeWb3/wmr776Kh988EG0+0ln4cKFmM1mYR1KZWUljY2NQtfm2UAKGckFwcu7m/EFw8zKTWZhUZoYrPkQBprBngbloselebCZLc3abJS7ZtwlxMJ+P+7XXgMgPSYbA3B4iyac5lydjyGmO6ri3bcAmLbsCpxpmij4yuXFALh3dqAGwphzkrCURKYKR7wxddVH6e7uxmKxMG+etuPphik3kGxJps3TxvY20b1vmzsX64wZqH4/7j/9SYw5zJQt0lq6D20Rlz4qBgNzP7MWgAPvi+UlAOcyrW3ds6czrhV70aJF0QF552LGg0Qiufi5//77eeGFF/jd736Hy+Wivb2d9vZ2RkZGAEhJSeHee+/l4Ycf5sMPP2TPnj185StfYfny5SxbtuwEZ/9kSCEjOe+oqsrvdmozU760vCjO48LeyATcuXeCWSw5vXz8ZVRULs+7nKJkMZMz9P77hNxuTDk5OK64Qoj1tA7RUTeAwaAwY7k4q8bvHaFymyaO5q1eG/331bOymeSwsNKvXZ9jWe7otWaUQtEV7GGOdqlz52KLlHusRis3TbkJgFeqXhEeS1EU0u7Shuz1vfR7oSsKYPZVWudW9a4OvB6xTbv86msxmkx01FbTXlMlxKxT0zCm21C9QUYOdAkxl8vF9OnTAdgX482RSCSSRDzzzDO43W6uueYacnNzo8fvf//76G2eeOIJbrrpJu644w6uuuoqcnJyeOWVVyY465lBChnJeWd3Qx913R6SLEZunhfTDTDUBZVadoQFfyWEguEgr9docw++MO0Lceft/1+tPTnltlvjWq6PbtVKLkVzMuJaro9v+4iAd4TUnFwmzxydR2M2GvjradlMwUhAQRiABzAy+4scoxSAyyKTdHVun6oNhfqg6QP6vOLm6+SbbkJJSsJfV8dwpNtJJ7skmYzJDoKBMJU7ROd/UnIK05ZpAm3/e28JMcWgRE2/QwlMvwsWLADgwIEDE07zlEgkFyfPP/88r0Uy0qcSGw9VVRMe99xzT/Q2NpuNp59+mt7eXjweD6+88spZ98eAFDKSC4CXd2uzUm6ck4vDGuM/3/8/EA7C5IWQIxpTt7ZupXukmzRrGlfli63EgZYWPJHZBakxkyVDwTCV2zVRMGtFfBvloY3aUsbZ13wmLju0JqRd30Y1QHdAFAAHg8WEMJFNF7k+MUMyPX06szJmEQwHeaPmDSFmdDpJuVGb2Nv/+z8IMUVRKL9Sy8oc+ag1LmOjl5cqt27B7x0RYo5F2WBQCDQPEegQ1x2UlpbidDoZHh7m+PHjca+BRCKRXCxIISM5rwz7g/z5gJYx+PwicY4Lqgr7tEF2XPaluPv+qVrzlNw45UbMRrMQ63/lVVBVkpYtw1Ignrf+YDfeoQBJyRYKy9OFWG9rCy3HjqAoBmZdHTPJNxDCfLwfgDfx88o+0bdScfAwAPM5jLLvhbjrvWPqHQC8Wv1qnCBJ/YK2EHLw/fcJxQyrm7YkG6PJQG+rh+4mMTZ5+ixSc3IJ+LxU7RD3LxmdFmzTNb+RZ2+nGDMao/uXZHlJIpFczEghIzmvvHWwHY8/RHFGEouLY0y+TTu0lmZzEpSLWRW3z82HTR8CcGvZrUJMDYXof1Wry6beEb/n4+jHmnCasTwnzuR7OJKNKZ5/Ga50sZNp5HAPqjeEN8nIXkL8YVdTVJB0dHTQ2tqKQVGYyzE4+gaMiCWktSVrsRltVPdXc6D7gBCzzZ6NZcoUVJ+PwfXrhZg1yUzJPO1a9EySjqIozLpKE1xHNn8Q91wdC7XWyeF9nahhUTzp5aXq6moGBgbi7iuRSCQXA1LISM4relnpcwvz402+Fb/T/iy/TWtxHsPbdW8TCAeYkT6D6enThZhn+3aCrW0YXC5cn/mMEBvq89F4WNtWPfNysawUDoU4HBEDs1eK9wPw7NY6fFKW5GIzG6nt9rC7QRMr+tyFadOn48guhZAPDooLIV0WF58p0s77WvVrQkxRFFJuuQUA92ti9xLA9GVanfn4rnZCMasHZl2pCZnGwwcY6BaNvbYZ6RiSTIQH/Piq+4VYRkYGhYWFqKoqzI2QSCSSiwkpZCTnjcaeYXbU9aIocPtlMQPpgj448pr29dw74+6rl5VuKb0lLjbwhjbILvnGGzDYxC6nql0dqCrklqaQmi3OnGk4WIGnrxe7K5nSheKag2C/F19NPwBpS3K5IbKV+9V9LYRCIQ4c0DIsCxYsgPmRVu8Dot8F4LNlnwXg3fp3CYTELqSUm28CRWF41y4CLWLZqmBWOnaXmZHBAE2He8X7ZWWTP2s2qCpHt3woxBSTAfs8bU2BZ098q7Weldm/f39cuUsikUguBs6rkPnJT37C4sWLcblcZGVlceutt1JZWSncxuv1cv/995ORkYHT6eSOO+6Qsy8uEV7Zpy1rvKJsEnmpdjFY9R543eDKhWKxdbqmv4ZDPYcwKSZumCJO8g17vQy+p5WHUm6+Oe4xj0e2Qk9bGu+kP/bRRgCmX34lRpPouRnZ3wUqWEpSMKXbuG2BZsB962AblVXVeDye0Smas2/XFls274TeOuE8i7MXk2nPZMA/wEctHwkxc14eSUs0AeV+I8YQbDQwLdKFdGx7/N6S8quuBeDw5g/iBInjMq28NHK4h7BXNCjPnDkTk8lET08PbW3x3U0SiURyoXNehcymTZu4//772b59O++99x6BQIDrrrsOj2e0w+Khhx7ijTfe4OWXX2bTpk20trZy++3xvgfJxYWqqry+Xxvzfev8yfE3OBjJZsy+Awxi6/Sfa/8MwBX5V5BuE826Qxs3EfZ4MOXlYo9kG3R62zSzrMGgUHpZphAL+LxU7dKG1c1YcU3c5QxXaCWbpPna/ZaXZpDpstI/HGDjNm2NfXl5OUajEVw5UBLpojoklpeMBiPXl1yvPY+6P8c9ztjyUqwg0ctL9Qe642bKTF26ApPFSl9rM+01YheSOd+JKcsOwTAjB7qFmM1mY9q0adqlHjoUdz0SiURyoXNehcw777zDPffcQ3l5OfPmzeP555+nsbGRPXu0Nwa3282zzz7L448/zqpVq1i4cCHPPfccW7duZfv27Sc4u+RC5kjbALVdHqwmA9eVi7s88Lqh8h3t67nifBhVVXmnXovdWHJj3Hndb2qZjJQbb0QxiD/eVbu0TF5BeTp2pzg7pnbvbgLeEZIzs8mbNkOIBTqHCbR5wKBgn62Zbo0GhZvn5mEkREdjDQBz5swZvdMcrQuJAy9r3VdjuHGKdt0bmzbiCYht0a7rrkOx2fDX1+M9IBqCJ+U7yZjsIBQMUxPThWRNSqJssTY98+iWjUJMURSSFkRMv/vF+4297oMHDxIOh+PiEolEciFzQXlk3G5tO3B6uvYpe8+ePQQCAVavXh29zYwZMygsLGRbZEZILD6fj4GBAeGQXHjo2ZhVM7Jw2cQyDkff0Myyk6ZDzlwhdKTnCE2DTdhN9rjZMSG3G8+mzQAk3ySWlVRV5XhEyExbHCOcgGMfbwRgxuVXxpmOhyu0N3/btDSMjtFrvWV+HvkGN4oaIiUlRVigxsybwWiF7kpoPyicb1b6LIqTi/GFfGxo3CDEjE4HrsjP+8BbMUPuFCVaXqreEy9IZqy4GoDjOz4mHLPTKSnik/HVugkN+oXY1KlTsVqtDA4O0tjYGHdeiUQiuZC5YIRMOBzmwQcfZMWKFdGNvO3t7VgsFmGTJmjbONvb430CoPluUlJSokdBzAwRyfknHFZ5c7/mx/hs7CRfGDXJzv183JK0t+u0vULX5F8TtyBy8L33UAMBrFOnYps+TYh1Ngwy0DWCyWKgOGZBpNczRN2+3QDMuOIaIaaqKsP7xbKSztz8FGbbNfFtyyoWBZAtBaat0b4++LJwP0VRot6et2pFsQKQfIM25G7gnfWoMRmS0su0acItlX0MD4iCpHjeAqwOB56+XlqOHRFipnQb5gIXqDBySCwvmUwmZs2apV3qQVF0SSQSyYXOBSNk7r//fg4dOsRLL730ic7zne98B7fbHT2amprO0BVKzhR7G/to6R/BaTWxcoY45p/BdqjTsirR8kyEsBqOlpV0n8lY3G9qnpPkm26Ki1Xt1LIxJXMnYbGZYmJbCQWDZOQXkllYLMQCzUOEerwoZgO2mRlCzOfzkRnWOoj2Dbnin6h+/Yf+CDGCRC+LbWvbRveIKCwcK1ZgcDoJdnQwEjOsLiXTTlaRC1WF2n0xQ+5MZsoWa1tmK7duibucpIiA04WZcKmR8tLhw4flygKJ5BLgnnvuQVGUuKO6ulqIWSwWysrKeOyxxyb8v//MM88wd+5ckpOTSU5OZvny5bz9triw9nw151wQQuab3/wmb775Jh9++KGQns/JycHv99Pf3y/cvqOjY9z9DVarNfpC64fkwuKNSFnpulnZ2MyikZejbwAqTF4EacVCaF/nPjqGO3CZXVwxWexkCnZ1MbxjB6C1XY9FDatURVqPpy5J1K20CYCZMdkYGFNWmpWBwRqzr+noUVDD9IVtbGjw0ecRMyRMvQ6sKTDQog33G0NhciGzM2YTVsN80CgOsjNYrbiu1WbDDLz9Ttw1lUWG3FXtTlBeWn4lECkvhcTykn2OllHy1w8QdPuEWHFxMU6nE6/XS01NTdx5JRLJxcf1119PW1ubcJSUlAixqqoqHnnkEdatW8fPf/7zcc+Vn5/PP//zP7Nnzx52797NqlWruOWWWzh8+HD0NuerOee8ChlVVfnmN7/Jq6++ygcffBB9gXUWLlyI2Wxmw4ZRH0FlZSWNjY0sX778XF+u5AwQCqv8+aBWVrp5foKy0lFtCSSzPhsX0stKqwpXYTGKZt3BDR+AqmKbMwdLvjiTpr3WzbDbj8VuonCW2OU0POCm6bBWTpkeEQE6alhl5KCWLdE9JmPRu3yGkvIIheG9IzGfPMw2mL5WfF5juK74OgDebXg3LuZaq91vcP161BhBUrpQu5bW6n48MYKkYPY8bK5kRgbcNB0Ry0SmVCuWIk3Yx3YvGQwGysu1BZlHjohlKYlEcnFitVrJyckRDmNkga4eKyoq4r777mP16tW8/nr87ymdm2++mRtuuIGpU6cybdo0fvSjH+F0OqONN+ezOee8Cpn777+fF154gd/97ne4XC7a29tpb29nZERbfpeSksK9997Lww8/zIcffsiePXv4yle+wvLly1m2bNn5vHTJabKzrpfuIT+pSWauKBO9Knh6oP5j7euZopAJhUO816DNh7mhRMy4AAy+q4kB13XxE3lr9mmllOK5GRhN4o98zZ4dqGqYzOIppObkCjF/8yChAT+K1Yhtqrg+YWRkhLo6bUbMnIgAePtQgjkssyID+478Ka68tLpIM/Xubt9Nr1cccue8/HIMLhfBri5G9u4VYskZdrJLkkGFmr1imchoMjF1SaS8tC1BeSkiyEYOxJeXdJ9MZWWlLC9JJOOgqirDgeHzcpzNoZV2ux2/33/iGwKhUIiXXnoJj8cTTSqcTnPOmcJ04pucPZ555hkArrnmGuHfn3vuuehq8CeeeAKDwcAdd9yBz+djzZo1/Pu///s5vlLJmeKdyJv9Z2ZmY47Zc0Tln0ENQc4cSBezc/s699Hr7SXZkszi3MVCLNTfj2fnTgCSY1YSqKpKbUTIlM6P8eMA1Tu1/2DTllweFxuJrDKwzUhHMYvXWllZSTgcJisri+uWTuPJj9v5qLob90iAFPuYLqzSVWBxauWl1r2QvygaKnAVMDN9Jkd7j/Jh44fcMe2OaEyxWHCtXo371VcZePsdkhaLz3nqomw66gao3tPB3JViBmr68is5uGE9VTu2cu1X78NoGv1vbp8zif43avA3DRLs9WJKH518XFBQgMPhwOPxUF9frw33k0gkAiPBEZb+bul5eewdX9wR1+QwEW+++SZOpzP697Vr1/Lyy2LzgaqqbNiwgfXr1/PAAw9MeL6DBw+yfPlyvF4vTqeTV199NfoB6HSac84U5720lOjQRQxoA7uefvppent78Xg8vPLKK+P6YyQXNuGwyvrDWvll7ZwE38MjkR1Ds+LXDuhtytcUXIPZILZrD364EYJBrNOmYSkuFmJdjYMM9noxWQwUxGy69g0P03BAM9NOXSoKGVVV8Ua6e+zloskXIv4YtMm4ZVkupmY5CYRUPjiWoLykdy/pKxfGoJeX9GzTWKLdS+++G19eigz0a6t24+mPKS/NmkNSSireoUGaj4hD7owuC5biFABGjvQIMYPBwMyZM7VLleUlieSiZ+XKlVRUVESPp556KhrTRY7NZmPt2rXceeedrFu3ji1btuB0OqPHiy++GL3P9OnTqaioYMeOHdx33318+ctfviB+V5zXjIzk00VFcz/tA16cVhMrYstKI/1Qq5lumSkKGVVVo4bYVYWr4s47Wla6Li6mZ2OKyjMwW0Szbt2+XYSCQdLy8kmfLLbpBzuGCfZ4waRgmy6WlXw+X9QQq38aWTs7h6oPqnn7YDu3LYjZGzXrFq1z6cjr8JkfCC3lqwtX8697/5UdbTtw+9ykWFOiMceyZRiSkwl1dzOyfz9Jl10WjTnTbGSXJNNRN0DdgW5mXzU6HdlgNFK6cAkHP3iX6t3bKJo7X7gce3kG/jo3I4d7cF0hTlWeOXMmu3fv5tixY9x4443RerpEItGwm+zs+OKOE9/wLD32qRBdm5KAlStX8swzz2CxWMjLy8MUydwuWrRIWCKbnT06d0vvcALNw7pr1y7+9V//lV/96ldCc87YrMxEzTlniguia0ny6WD9IS29uGpGFlZTzBvk8XcgHIDMGZApzoA52nuUVk8rdpOdy/PEzEloyIPnY81Xk8gfUxtZLTDlsnizbtWOrQBMXbI8bgiePmvFNjUNgzWmXbuqimAwSHp6OllZWrlqbWSJ5KbjXXh8Mf6SstVgskN/A7TtF0LFKcVMTZtKUA3yYVPMwkezGec12pC7wffFwXkAUyJzbeoq4v0ueht29a7tcXV1+ywtw+SvdxOKWXVQXFyM3W5neHhYDseTSBKgKApJ5qTzcsT+nvok6CKnsLAwKmJA88qUlZVFD5crwWiJCOFwGJ9Pywifz+YcKWQk5wRVVXk7ImSun52orBRxy8+M71bSy0or8lbEfSLxbN6E6vdjKSrCOnWqEOtt9dDXPozBpFA8W8wABfw+aiu0IXjTlq6Ie8yRQ1rZxV4+KS42tqyk/2KZkeOiOCMJXzDMh5UxbdEWB0yNiCy9fDaGzxRpsXfrE3QvrdKWQQ6+/36cICmZp11bc2UfvhFRPBXOnofZZmeot4eO2mohZkq3Yc51gAreo2J5yWg0MmOGtqLhQkgZSySSC4PvfOc7bN68mfr6eg4ePMh3vvMdNm7cyN133w2c3+YcKWQk54SjbYM09g5jNRm4ZnpMdsQ3CNXva18naLueqKw08K7mLXFdd13cp5XayAyYgpnpWOxiVqVh/z6CPh+uSZlklZQKsWDPCIF2DxjANlP01QQCAaqqqgCifhLQPqXpWRldsAnovp+jb8SFPlOoCZntbdsZDgwLMccVV6BYLAQaG/FXi4IkLcdBWk4S4ZBK4yFRkJgsFkrmLwSgeld8x4Du+9ENzWPRn9fRo0fl7iWJRAJAZ2cnX/rSl5g+fTrXXnstu3btYv369XxmTIPFE088wU033cQdd9zBVVddRU5ODq+88spZvzYpZCTnBL1b6eppmSRZYqxZVe9qu5XSp0D2bCHUMNBAdX81JsUUt1sp7PUytFmbApzIH6O3XU+Zn6CstFMvK10eX1aKvLlbp6QKu5UAamtr8fv9JCcnM3my6C+5vlzLNG2q7MIfjBEAUz8DBhP0VEG3KEhKU0spcBUQCAfY2rpViBmdDhyRtOzgBnFwHkBJpJ26NsG0Xn2JZPWu+BkOtkimyVvVT9gvGomnTJmC1WplaGiI1tbWuPtKJJILn+eff57XXnvtlGPj8eyzz1JfX4/P56Ozs5P3339fEDFw/ppzpJCRnBPejQyLS1hW0rMUMz8bt1tJLystzlksGGEBPB9/jDo8jCkvF9vsciHm7hqhu2kIxaBESzA6oWCQmj2aWU+fuTKWkQm6lY4dOwZo8xFiBdCcySlkuqwM+YLsqIvJdNhSoDgyjfi4ONZbURSuKbgGIM4nA+CMTPkd3BDvkymZrz23hkM9hAKieCpZsAiD0UhPcyO9rS1CzJyThDHdBsEwvuN9QsxkMlFaqmWpKisr4x5TIpFILiSkkJGcdZr7hjnWPohBgZXTY2a5hAJQHXmDnnFj3H11IXNt4bVxMd0A61q9Ok5U1EUyFHlTU7E7xSnATUcO4vN4SEpJJW/6TCEWGvDhbxwE4oVMOBzm+PHjgNaGGIvBoLB6pvb8NhyNXx/A9Mggv8q340IrC1YCsLl5M6GYzdWulStBUfAePEggZh5DdlEySSkWAt4QzTGCxOZwUlCubQ+v2S1mZRRFiZp+E5WX9OcnhYxEIrnQkUJGctb54Jj2pr6oKJ00hygqaNgKvgFImgSTFwqhDk8HB7oOALCycKUQU8NhhjZp7dq6IXYs9ZER/LHZGICa3Vo2pnTRUgwGsXtq5Ig2YddS6MKYbBVira2teDweLBYLRUVFCZ/rtTO0VsX3jnTET+GcFll02bgNhsVJvvOz5pNsSabf18/+LrGzyZSZiX3+fAAGPxDLS1rGaYLupUXjl5eiPpljvaghMZszdepUFEWhs7MzbteZRCKRXEhIISM567wfyU5cOzN+si7HI0sRp62BGFGxsWkjAPMy55GVJN7Xe+AAod5eDE4nSQsvE2K+4QCt1W4AiueIQkZVVWr37gKgdOGSuMvxHtMERuymayCajSkrKxPaFceyomwSNrOBlv4RjrUPisG0IsgqBzUMVeIAPLPBHF2EqT/vsehLJIcS+GSmRMRa3f5u1LAonkoXaRNIW6uO4ekXMzaWomQMDhPqSBBfnVuIJSUlUVCgzdaRWRmJRHIhI4WM5Kwy5AuyvUYrXVw7M1sMqupomUWffjuGTc1axkX3j4xlcONGABxXXoFiFg25jUd6UcMqabkOUjLFdu2e5kYGujowmS0Uzp4nxML+EN7qfgDsMd1KMPqGPm3atLiYjt1ijO6Q2nA0wfp6fYlk5VtxIb28tLF5Y1zMea2WdfLs2EFoYECITZ6ehsVmZHjAT0e9GHNlTCKndCqoajQTpaMYlKhgk+UliURysSKFjOSs8lFVF/5QmKKMJEozHWKwuwr66sBg1nYSjWEkOMLOdm1/0tX5V8edd2hjpKwUs6cLoD6ysbp4TnxWRc/GFMyei9lqE2K+WjcEwxhTrZiyxX0m/f39dHR0oCgKU2Pm1cSyOiLY3pvIJ1O9AYLigrYVk1dgUkzUueuod9cLMWtJCZbSUggGGdosLoM0mgwUztaea13C7qXIcLzdCcpLEZ+M92hvXClMFzL19fV4vd5ET1UikUjOO1LISM4q0bLSjOz4qZR6907xFWAVp0fubNuJL+Qj15FLWao4YjvQ2orv2DEwGHBcFdOSHVZpiMxUiS0rAdTu1cTRlMsSlJWOjlkSGXOtelkpPz8fh8MRd9+xrJqhlcH2N/XTORgjAPIWgDMb/IPQ8JEQcllcLMrRlkrq2SghvipSXvogQRv2XO251h+Mz6xMiZTQmg4dIOAX9zJZy1LBpBDq9xHsFGfYTJo0iYyMDMLhcHQlg0QikVxoSCEjOWuEwiofRoy+qxP6Y9Zrf+rlljFsbtbmw1yVf1WcqNBNvvb58zGliXuQOmrd+DxBrEkmcqYkC7GRoUFaK7X26SmXLRJiqqqO+mNmxJeVJupWiiUr2ca8glQAPojNyhgMo2W0BN1LE7ZhR9YVDH38cdwSycJZGSiKNs14sFcUT5MKinBmTCIY8NN8+KB4ORYj1inatXqPiR4aGC2jyfKSRCK5UJFCRnLWqGjqp8fjx2UzsbgkRhwM90JjpNQR449RVZXNLaNCJhbdH+NMWFbSMhKF5RkYjOKPd33FHlQ1TGZhMcmTRGEVaPMQcvtRzAZspeK8Gp/PR11dnXapE/hjxrI6kpV5/0Rt2DHlHF3I7OvcR7+3X4jZ583DkJJC2O1mZP8BIWZzmsku0a67IWbKr6IoTFmgCbfafbviLsceWYrpreyNi+nCraqqilCMeJJIJJILASlkJGeND45pZterp2VijhEVVG8ANQSZMyGtWAhV9VfR7mnHarSyOGexEAsPDzO8TRNArpXXxD1m1B8zd3x/zJRE3UqRN3FraSqKWeyeqq2tJRQKkZaWRmZm/JTgRKyepflkPqruwhuIEQAlV2tLJN1N0HFICE12TmZa2jTCapgtLaIXRjGZcK7Q9kINbY4vPRVFPEGxQgagZIH2Otbu3R3nhdEzUL76AcJecWdTQUEBNpuNkZERmpubJ3zOEolEcj6QQkZy1tCHwq2O7VaCUX/M9OvjQnpZaUnOkvglkdu3o/r9mCdPxhKznn6ge4TeVg+KQaFwVswwu1CIusiSyCmXieIINLMrxO9WgtGy0rRp0056++yMHBeTU+14A2G21nSLQUsSTIkYmKviF0XqWahYIQPgvFqL6asZxlIUMfw2H+slGCOeCmfPxWgyMdDVQW+LKEhMGXZMmXYIq3hjhuoZjcaouVmWlySSi4d77rkHRVHijurqaiFmsVgoKyvjscceIxgMnvjEwD//8z+jKAoPPvig8O9er5f777+fjIwMnE4nd9xxBx0dCbo3zzBSyEjOCmOn+cYtiQwFoCqyJHJavD9mS7P2Bp6wW+nDjYBWVooVFXpZKbc0BVvMjqTWyqP4PB7srmRyysTyUGjIj79Jm/kS64850TTf8VAUhZUztOe9sTK+k4iy1dqf+uswBn2ezNbWrXFTfh1XXAGKgu/IUQIdYtlqUr6TpBQLQX+Y1qp+IWax2cmfNQeAugTlJdt07Xl7K8f3yVTHLK2USCQXNtdffz1tbW3CUVJSIsSqqqp45JFHWLduHT//+c9PeM5du3bxq1/9irlz58bFHnroId544w1efvllNm3aRGtrK7fffvsZf16xSCEjOSvob94Li9JITYqZ5tu0E3xusKdDvmi6dfvcVHRVAPH+GFVVGZrAH9MQbbtOMM030q1UMn9h3DRfb2UfqGDOdWBKEaf5dnR04PF4MJvNFBYWjv+EE3D1NM0ns+l4AiEzNbJsrWkHeMVhdPMy5+Eyu3D73BzuOSzETBkZ2OZogsSzRczKKIoSzcokKi+N+mR2x8V0Aeet7I0fqhfZu9TZ2Ynb7Y67r0QiuTCxWq3k5OQIh9FoFGJFRUXcd999rF69mtdff33C8w0NDXH33XfzH//xH6TFNFq43W6effZZHn/8cVatWsXChQt57rnn2Lp1K9u3x49+OJNIISM5K2yOvHlfPS2Bp6Q6koUouzZumu9HLR8RVsOUpZaR68wVYr5jxwh2daHY7SQtFX0uAd/orqGiCebHTOSPSdStpGchpkyZMu403/G4vDQDs1GhoWeYum6PGEwrhoypmk+odqMQMhlMLMvTVgt81CK2aAM4Iy3nQ5viy0vFsyNLJBO0YZdESmotxw7jGxZbra3FyShWI+GhAIHWISGWlJQU3fQt27Aln3ZUVSU8PHxejri1J2cQu92O3++f8Db3338/N954I6tXr46L7dmzh0AgIMRmzJhBYWEh27ZtO+PXO5ZT+80skZwE/mCYrZFpvnpWQqAmsiSyNH5H0ti261iGtmhv6o6lSzFYxCxPy/E+wkEVV4aNtBxxmN1AVye9LU0oBgNFcxcIMTU06guZSMiUxfhxTgaH1cTi4nS21vSwqbKTkkkl4g3KVkNPlbauYNYtQuiKyVfwXsN7fNTyEd+Y/w0h5rz6Krp/8Qs8W7ei+v0oY16L/BlpGIwK7q4R+juGSR0z2C8tJ4+03Dz62lppPFjB1KWXR2OKyYCtLJWRwz14j/ViyRfn+kydOpWWlhaqq6u57DJxJYRE8mlCHRmh8rKFJ77hWWD63j0oSUknvmGEN998E6fTGf372rVrefnll4XbqKrKhg0bWL9+PQ888MC453rppZfYu3cvu3bFl6YB2tvbsVgspKamCv+enZ1Ne8yy2zONzMhIzjh7G/sY8gXJcFgozxNnuTDUCW2RpYgx03xD4RAft34MJBYyno8iQubKK+JijZFlj4Wz4ofZ1e/fC0Du1BnYHE4h5m8aQPWGMCSZsBSIb95er5empibtUiPllVNF9wdtTFheinxyqd4Q14a9Ik/rTjrUfYher9gWbSsvx5ieTtjjYXjvPiFmsZvILUsFxulemj9+G7Yu5EYS+GR0IVdTUyPbsCWSi4SVK1dSUVERPZ566qloTBc5NpuNtWvXcuedd7Ju3Tq2bNmC0+mMHi+++CJNTU387d/+LS+++CI2m22CRzw/yIyM5Iyjl5WunDoJgyGmy6cmMugtZw64xG6mIz1HcPvcuMwu5mWKe5BCQx6G92qCxHlFAiFzeHR+TCy6kCmZF59J0LMx1rJUlJhrraurIxwOk56eTnp6fLbmZLh6WhY/fusY22p68AZC2Ma2dhddobVhD7ZC5xHILo+Gsh3ZTEubxvG+42xt3cpNU26KxhSDAeeVV+L+058Y2rwZx7KlwmMWzc6gpbKPhsM9zLu2QIiVXLaYvW+/Tt0+rQ17rOjTDb+B5kFCQ36MztFMT15eHna7PdqGPd72b4nkUkex25m+d895e+xTweFwjJtNXrlyJc888wwWi4W8vLxo6XzRokVUVFREb5ednc2GDRvo7OwUsrGhUIjNmzfzi1/8Ap/PR05ODn6/n/7+fiEr09HRQU5Ozild96kiMzKSM45ubr06tlsJRstKZfE1Vj0bszR3KSaDqLGHd2yHYBBzYSGWmDdRd9cI7s4RDAaF/OmiAS0UDNJwsAKA4kRCJtLdY5uWFhf7JGUlnWnZTnJTbPiCYXbUxQycM9u09Qww6hsag969lNAnE23Djp8no++Yajnehz9mLkz+zNmYrTY8/X101tcKMWOyBXOeA1Ti2rANBkM0KyW7lySfZhRFwZCUdF6Okx3/cDLoIqewsFDw/9ntdsrKyqKHy+Xi2muv5eDBg0J2Z9GiRdx9991UVFRgNBpZuHAhZrOZDRs2RM9VWVlJY2Mjy5cvP2PXnQgpZCRnlK5BH4dbtQ3MV06NETLhsFZGgYT+mG2tmiHs8smXx8WGImWlRNmYpiNaNianNAWLXRRAbdWV+EeGsbmSyZoilodCngCB5kjb9VRRyKiqGjW2fhIhoyhK1PC8sTLBlF+9e6nqvbhQtA27ZSthNSzEHCtWgNGIv7qGQEuLEEvNTsKVYSMcVOPasE1mMwXlWtdTwwGxLAWjgs4Xcz8YfR2kkJFIPl24XC5mz54tHA6Hg4yMDGbPng1ASkoK9957Lw8//DAffvghe/bs4Stf+QrLly9n2bJlZ/X6pJCRnFG2VGnZmNmTk5nkFFuZaT8Aw91gcUKBWA4Z9A+yv0vzzizPFdW7qqp4tpzYH1MwK778U1+hlZWK5y6Ia7v2VWtt16bsJIwxbdc9PT309/djNBopLi6e6CmfEN0nk7ANW89MNW4H36AQmp81H4fZQZ+vjyM9R4SYMSUFe6QNe2jrViGmKEr0tWg6Gr92oGiulplqOLA3LmaNCDpvVV9ch4QuZNra2hgaGoq7r0Qi+XTzxBNPcNNNN3HHHXdw1VVXkZOTwyuvvHLWH1cKGckZRX+zvio2GwOj5ZOSq8Akdh3tbN9JSA1RlFxEvitfiAUaGgg0N4PZjGOJ2D4dCoZpjiw7LJrAH5OwrHS8HwDb9PHLSoWFhVhiOqROlcvLJmEyKNR2eWjqFdueySiFtBIIB6BObKc2G8wsy9U+ySSa8uuIrCvwxAgZgMLIhOKmI/FCpnie1rnVcuwIAZ+4YNJalIxiNmht2O3itTqdTnJztZZ4mZWRSC5snn/+eV577bVTjp0sGzdu5MknnxT+zWaz8fTTT9Pb24vH4+GVV1456/4YkEJGcgYJh1W2VGlD6RLOj6n5QPuzbIKyUl6CslIkG5O0cCEGh0OItde6CfhC2F1mJuWLHUnDA2466rQ33Li2a3VM2/XUs+OP0Um2mbmsSHuMxN1LJy4vJfLJ6EJmeOu2uG3Yk6enoSjQ1z4ctw07LXcyrkmZhIJBmo+KA/cUkwHrFG35pK9q/O4lKWQkEsmFghQykjPGoVY3vR4/Tqsp+sYdxTugTbGFhP6Yj1s0o28iIaO3XTsTlZUOj5aVYruOGg7sA1Uls6gEZ5pYdgq0DxMe1LZdW4vFbdeBQID6+nrgzAgZGBV2mxL5ZMoiQiZBG7YuZA51H8LtE6fq2ufOweB0EnK78R4RS082h5msYq31Pba8pCgKxRFhd6LyUtyljmnDDofDcXGJRCI510ghIzlj6G3X2kTbmB+tus0QDkJ6KaSLg+GaBppoHmrGZDCxJEcsHYV9Pjw7tfUCjkRt1xGjb+ySSJi4rOTT266npKCYxWttbGwkGAzicrnIykow0O800IXMtpoeAqEYAVC8AgxmcDdCr9hJlOPIoTi5mLAaZle7OPtFMZlIirReez6OLy8VRMpLzRP4ZOr3T2D4rXOjxiyfzM/Px2q1MjIyQltb27jPVyKRSM4VUshIzhgn13adIBsTabuenzmfJLM4tXJkzx7UkRFMmZlYp4nLHocH/HQ3aabTgpit1Wo4PLE/JpJtsJ6g7fpMtTvOyk0m3WHB4w9R0dQvBi2OUfNz7Ydx912ep5mf9fLbWByXaxksz8cfx8VGDb99cfuTCufMA0Whp7mRwV5xO7cp044xxQJBFV/dgBAzGo3RpXO1taLokkgkkvOBFDKSM8KAN8Dexn4ggdFXVcfsV4qfH7O1VcsmJPTHfKS9QTuuvDJOVOht15mFLpKSRUNuV2M9w+5+zFYbedNnCbGwP4SvTivTTDQ/5nSn+SbCYFC4vFTLGuk+IoHSa7Q/axIImUgX1/a2+MVrTt0nU1FB2CPuc8ouScZsM+L1BOhqEjui7E4XOaVTAWg4UCHEFEWZsLw0ZcoU7VLl3iWJRHIBIIWM5IywtbqbUFhlSqaDgvSYXSA9NdDfCEbL6AC4CIFwgJ3tWuko0fwYT2TZmJ55GEvj0QnariPZmILyOZjMZiHmq3VDSMWYasU0SZyU6Xa76erqQlGU6Bv2meLKqdpCx4+qEhh+p0TWNdRtgZA4xG5RziKMipHGwUZahsSZMebCQsz5+RAI4InZgWI0GqIDAhO1YY/6ZBKUl6bq82TGFzJNTU0nXDInkUgkZxspZCRnhAnbrvWyUuFyrYwyhgNdB/AEPKRZ05iZPlOIBXt78R09CoBjuThQSVVVmo9qb7KxZSWAxkPaTJrYbiUYfXO2TUuLy/LoWYbJkyeTdArL2U6GFWWakNnf7GbAGxCDefPBlgI+N7RVCCGXxcWcSdrMmNjykqIoY8pL4/tkEs+TGRUyaoxx11qWCopmig4NiGIlIyOD5ORkQqEQjY2N4z9hiUQiOQdIISM5I3xUrZVLrpo2KT6ot13HLImE0bLSsrxlGBTxx3F4h9blZJ0+HVOGaObtbfMwPODHZDaQM0VcTBn0+2mJtBUXzZkf95je6n7tvJHlimPRfR9nOhsDkJ+WRMkkB6GwyvaamIWOBqM2XwcSl5cm8slMME9GFzJt1Vqb+lhyp87AbLMzMjhAZ0OdEDM6zJgna+3sseUlRVGiZTdZXpJIJOcbKWQkn5jGnmGaekcwGRSWlMR0D4UCUB8xopaujLvv1pbx/TGerZGyUoI9HXo2JrcsBZNZnNjbevwYwYAfR1o66ZPFpYmhQT/BjmFQwFqaKsTC4fBZFTIAV0SyMrrwE5gSeX0mMPzuaN8Rv65g2VIwGPDX1BCI6SRKybJr6wpCKi0x+5OMJtPE6wr08lJE+AmXGnl9pOFXIpGcb6SQkXxiPq7R3pQXFKbitMYsVG/ZC/5BsKdD9hwh1O/t53CPljmJXUsAY/wxy+P3dDRXam/K+TMSlZUqACicPS+udKS/KZvznBgdonems7OT4eFhzGYz+fnidOEzxYqJhIwu9Jp2gk9cATB70mwcZgdun5ujvUeFmDElBdscbd9JbFbmROsKJponY5uaCkTWFcR0PemdSx0dHXJdgUQiOa9IISP5xHwceVO+vDRBWakusp255CowiD9u29u3o6JSllpGtiNbiPmbmrS1BCYTSYsWCbFQKBzNLiT0xxzU/DGFs+fFxaJlpZhsDEBdnVZeKSoqErbBnkmWl2ZgUKC2y0Nr/4gYTCuB1EJtXUGDKEjMBjOLsxcDictLevdSIp9MdF3B0Xjjrj5PpuXYEQJecQKwpTAZxaKvKxA7opxOZ3T0uP66SSSSC4d77rkHRVHijurqaiFmsVgoKyvjscceIxgMjnu+devWxZ1rxowZwm28Xi/3338/GRkZOJ1O7rjjDjo6Os72U5VCRvLJCIdVtkb8HldMTSBkajdqf065Oi60s03rVtL3CY1Fz8bY58+LW0vQWT9IwBvC6jDFrSXweoZor6kC4oWMqqrRjIztHPtjdFLsZuYVaI/9UWwbtqJMWF5alqe9Tttb49uwx/pkYo270XUFbR6G+mLXFeSRnJkVWVdwSLwckwHrFO1aE23Dlm3YEsmFzfXXX09bW5tw6NlUPVZVVcUjjzzCunXr+PnPfz7h+crLy4VzffSRuDrloYce4o033uDll19m06ZNtLa2cvvtt5+156cjhYzkE3G0fYBejx+Hxcj8yBt0FL9HK5MATLkm7r76pNrYab4wpqy0LIE/5phWIsmfHr+WoOnIQVQ1TFpePsmTxA6qYPcIIbcPjAqW4hiDcDAYXUtwNoUMnMAno5eXJjD87u3cy0hQzObY587F4HAQ6u/He0QsPZ1oXYHevVSf0CeTCkw8T6a2tjZuU7ZEIjn/WK1WcnJyhMNoNAqxoqIi7rvvPlavXs3rr78+4flMJpNwrkmTRj+8ut1unn32WR5//HFWrVrFwoULee6559i6dSvbt8d/+DqTSCEj+URsrdayMUtK0uPXEjRs08okKYVa2WQMncOd1A/UY1AMLMxZKMTUcJjh7VrHkuPyeCGjvxkXzIwfZjdRWclX0w9oG54NFtEg3NLSQiAQICkp6YytJRgPXch8XN1NOMZ7QsnVgAJdR2FANO6WJJeQnZRNIBxgX4coOhSzmaSlkXUFE3QvJSwvRTq7miIt62PRO7v8DQOoQTHTU1hYiNFoZGBggJ6enrj7SiSXIqqqEvCFzstxNj8w2O32E86FqqqqIi8vjylTpnD33XcL4xf27NlDIBBg9erRoaczZsygsLCQbdviy+FnkrNjBJB8atCzCrqJVUAvj0y5WiubjEEfgjcjfQbJFjE74qusJNTXhyEpCfsc0SDs9wbpqNXG5ic0+h6sAKBoTgIhEymPTNR2XVJSgsFwdvX9gsI0kixGejx+jrYPUJ43ZmllUjrkztNmydRtgnl3RUOKorA8bzmvVb/GtrZtcQMEHcuXM/TBBwxv3w5f+2shlj89jd1v1dNS2YeqqoIJuqB8LjA6DTkpJTUaM2UlYXCZCQ8G8DcOREtNABaLhcLCQurq6qipqRE+nUkklypBf5hf/+2m8/LYX/vXqzFbjSe+YYQ333wTp3O0/L527Vpefvll4TaqqrJhwwbWr1/PAw88MO65li5dyvPPP8/06dNpa2vj+9//PldeeSWHDh3C5XLR3t6OxWIhNTVVuF92djbt7e0nfc2ng8zISE4bfzDMzjotO5LQH6MbfScoK+kG1rHobddJixejxEzlba3qJxxWSZ5kIyVTnMo72NtNb2szimKgYNZcIaaGVbw12lqCcz0/JhaLycDSEk2EfXyK5SXdT5RwnkxkgeTw3r2EYz5Z5UxJwWg2MDzgp699WIglJaeQWVgMaKW5sSiKEjVGe2UbtkRyUbFy5UoqKiqix1NPPRWN6SLHZrOxdu1a7rzzTtatW8eWLVtwOp3R48UXXwQ0EfT5z3+euXPnsmbNGt566y36+/v5wx/+cL6eXhSZkZGcNvsa+xgJhJjktDA92yUGPd3QHnlT1Ae9jSHqj8lN4I+J1FMTlZWaj03Qdh0pK2VPKcXmFE3AgZYhVG8QxWbEMlm8Vp/PR0uLNvr/XAgZgCumZvJhZRdbqrr52lUxO52mrISPntCM0qoqZLN0IVPZV0n3SDeT7KMC0lJWhnHSJELd3YxUVOBYMvraGs0GcktTaD7WR/OxPtJzRQN1wex5dDXW03hoP9OXXynEbKWpjFR04YsIQeFSp0xhw4YN1NfXEwqFovV3ieRSxWQx8LV/jW9eOFePfSo4HA7KysoSxlauXMkzzzyDxWIhLy8v2qm5aNEiKioqorfLzs5OeP/U1FSmTZsW3U2Xk5OD3++nv79fyMp0dHREOxzPFjIjIzltxrZdx22Jrtus/ZlVDk7Rc9LuaadpsAmjYuSyLHEzddjvZ3j3bgCSEg3C042+MxL4Yw5N0Hat+2OmpKIYxWttaGggHA6TlpZGWlr8ec8G+t6lnXW9eAPixF0KloLJDkPt0CkadzPsGUxPm67dN9L1paMoCo6IT0b3GI1Ff81aKuN9Mvpr1pjIJxPJyPibBgnHTgfOzcVut+Pz+WhtbU34XCWSSwlFUTBbjefliPs9+wnQRU5hYaEwbsJut1NWVhY9XC5XwvsPDQ1RU1NDbm4uAAsXLsRsNrNhw4bobSorK2lsbGR5gt/lZxIpZCSnzceRtusVZRnxwWjb9TVxId0fMytjFk6LmDkZqahAHRnBOGkS1qlThdjwgJ+eFm2eSayQUVU16o8pTLCW4Hy3XccyNctJlsuKLxhmb0OMsDDboCjyH18XhGNYmquJFf11HEtSpLzk2REvZCZHFki2HO+LMxnnz5yNYjDQ397GQHenEDOl2zCm2yCs4qsXszIGgyHazinbsCWSS5e/+7u/Y9OmTdTX17N161Zuu+02jEYjf/EXfwFASkoK9957Lw8//DAffvghe/bs4Stf+QrLly9n2bL4ERtnEilkJKfFoDdARVM/MJ7Rd6P25wTzYxbnxPtjhvWy0rJlcZ8+miu1bMykAid2p0WI9bY2M9TXi9FsJm+6uHxSDYSib8AnMvqeKxRF4fJSTQBuq03Q8VMcKe/Ub4kLTSRkHJFfGCP79xMeFr0wWYUuLDYjvuEg3U2DQsyalETOFE04Nh0WfTIA1imaIVnv/BqL/rrp7esSieTSo7m5mb/4i79g+vTpfOELXyAjI4Pt27eTmTk65uKJJ57gpptu4o477uCqq64iJyeHV1555axfm/TISE6LHbW9hMIqxRlJ5KfFbInurYP+BjCYoCh+h9KE82NOYr9SwQTdSpOnz8JssQoxX8MABFWMyRZMMQbhoaEhOju1DMS5FDKgTfl9raKVbbELJCHSho0mZMIhbalkhMuyLsOoGGkabKJtqI1cZ240Zs7Px5yXR6C1leE9e3BeOep3MRgN5E1Lo/5AN82VfWQVid1ihXPm0VZdSePBCsqvvlaI2cpSGd7dkXDvkv66NTU1EQgEMMcYtCUSybnn+eefP63YeLz00ksnvI3NZuPpp5/m6aefPuXzfxJkRkZyWuj7lS5PlI3Ru5UmLwKrWF9tHmym1dOKSTGxIGuBEAsNDjJyUMsGxO5XUlWVppPxx0xQVrKWpcZlefTx+jk5OThiJgifbfSVDhVN/Xh8MaPBc+eBNRm8bmg/IIScFiflGeVAfFZGURSSIlkZT4IhVPnTx/fJ6G3YjYcPxM2r0H0ygTYPIU9AiGVkZOByuQiFQjQ1NY37fCUSieRsIIWM5LTQjb5XJCwrnbjtunxSOUlmMZMzvGsXhEJYioow5+UJsYFuL0O9PgxGhdyY8lA4FIqWQ4om2q90gfhjdArSk5icaicYVtkd65Mxjslm1cWXl/Rur4TlpYgITGT41X0yrdVuQjED7vKmz8RoNjPU20Nfm2jcNbosmLKSQAVfreiTURQlmpWRe5ckEsm5RgoZySnTOejleMcQigLLp8QYfcPhMfNj4v0xE68l0DIISQnarvUlkdnFyXEDoTrqqvENe7A6HGRNEVuZw94ggRZtO7NtgkWR57qspKP7ZLbWJJgno7etJzD86q/fjrYdcdmTpCWah8Z75Aghtyg6MvIc2F1mgr4QHfUDQsxssZI3TfMXJepe0o3SiXwyxcXFgPTJSCSSc48UMpJTZketVuKZmZNMmkM03dJ1FIZ7wJyklZbGoKpqNIOQyOjr2aaN1k+0X0kXMnpGYSz6/JiCWXMxGESR46t1gwqmSXaMKaJ3pq+vj/7+fgwGA4WFheM+37PJ5ZGOr8Q+mYiQadgKIbGcMz9rPiaDiY7hDpoGxXKOOTsLy5QpoKpalmsMikFh8rQJ2rAj5aWE6wpKT2z4bWlpwefzxT8XiUQiOUtIISM5ZfQum+WlCdqu9TJIwVIwiSKnabCJjuEOTAYT87PmC7FgTw/+6hpQFJKWiCJHVVVaj/cDkDctNe4hGw9VAJpZNRa9DKJ33YxFzx7k5eVhtVrj4ueC5VO00tyhFjfuEVGskFUO9nQIeKBV3K1kN9mZl6k93x3t8SUkfcqvnuUaiy4G9eGCY9Ffw8YjB+O2aFunpIICwa7I8s0xpKWlkZqaSjgcFvavSCQSydlGChnJKbM9kj1YFltWgtF24ZIr40J6NmbupLnYTWL30PBOLWadPh1TzFA6d9cIQ30+DCaFnBhBEgwEaK08BpxgUWTp+ELmfJWVAHJSbEyZ5CCsEl33EMVggOIrtK/1ct0YluZE2rDbEsyTWRox/O4Y3/DbXucm4BcH3GVPmYrZZsc7OEBXY714OXYT5sna3B9vjE8GkD4ZiURyXpBCRnJKdAx4qe32oCjaxmuBcBgaPta+Lo5fSzBhWSkiZGKzMUA0G5NdnIw5Zmt1e1UlwYCfpJRU0vPyxcsZDhBo1wbojV12CFqWR3/D1f0d54tlpSdRXkrgk9Ffx53tOxP4ZBaDouCvriHY1SXEUrLsONOshIMq7dWiIDGaTOTP1DqiJpryO1EbthQyEonkXCKFjOSU2B4pK5XnJZNij5kX0nEIRvrA7IC8+UJIVVV2t2urBxIZfYd3al4OfcT+WJorx/fH6EsOC2bNiWut9tVF/DFZdowusczV19fHwMAABoOBgoKC8Z7uOeGkDL9NOyHgFUJzM+diM9ro9fZS0y9O1TWlpWGdOQMAz474Fm09K9M8wbqCpsMH4mK6YdpX0x8nnnRB2N7ezsjISPxzkUgkkrOAFDKSU0IXMnHdSgD1H2l/Fi0Hoyhy6gfq6RrpwmKwMC9LLAEFu7rw10T8MQsXCjHNHxMRMtMmEDLlc+Ni+pLD2GwMjJaV8vPzsVgscfFziV6iO9Y+SK9H3FrNpGngzIagF5pF467FaInO4knok4mUl4YTlJcmzzgJIXPkEKGgON/GUpwMRoVQv49QjyiskpOTycjIQFVVGhoaxn2+EolEciaRQkZySmw7GX9Mcbw/Rm+7npc1D6tRNNbqnTXWGTMwjtmaCuDuHMHj9mv+mBJxEm3Q76f1uLZUsaB8Ttxj+mr7tfMmMPpeKGUlgElOa3R7+PbYdQWKMnEbtj5PJoFPJmr4TTRPJiIKuxoG8I2IYiWzsBibK5mAd4SO2iohZrAYsRRq1+qdoHtJlpckEsm5QgoZyUnT5h6hvmcYgwKL4/wxIajX/THjG30n8sc4lsSXnPS265ySFEwx/pi26kpCgQCO1DTScicLsdCQn0C7tmsoVsioqhrNyFwIQgZGO8AS+mQm2Lukl+l2d+wmFBaNu/aFi8BkItDUhL+5RYi50m2kZNlRVaIZLx3FYKBwliYMGw9NXF6KRQoZieTC4J577kFRlLijurpaiFksFsrKynjssccIxmRgY2lpaeEv//IvycjIwG63M2fOHHbv3h2Nq6rKo48+Sm5uLna7ndWrV1NVVTXBGc8MUshITho9WzB7cgrJthh/TPtB8LnB4tLG649BVdVoRmZxdoJFkREPR9LSREKmH4DJCdqu9Wm+BeVzE/tjAFN2EsbYBZO9vQwODmI0Gs+7P0bnpHwyzbvA7xFCszJm4TA7GPAPUNlXKcSMTgf2OZogSVReyo/srEpUXiqI+mQmMPzWuMf1yXR2duLxeJBIJOeP66+/nra2NuHQP2zosaqqKh555BHWrVvHz3/+83HP1dfXx4oVKzCbzbz99tscOXKE//f//h9pY7pMf/azn/HUU0/xy1/+kh07duBwOFizZg1er3fc854JpJCRnDTba7T24MT+mEi2oOhybbz+GGr6a+j19mI1WpmbKXpZAp2d+OvqxvXHtExo9NWyBQWzEpSVIv6Yiab55ufnXzALDpeWZKAoUNPloXMg5j99WjGkFEA4CI3bhJDJYGJhtva6JWzDnmCeTP5E82Rma9+nlsqjBP2ib8dS4EIxGwh7AgQ7xQ3bDoeDrKwsQE75lVyaqKpKwOs9L0fsB4cTYbVaycnJEQ6j0SjEioqKuO+++1i9ejWvv/76uOf66U9/SkFBAc899xxLliyhpKSE6667jtLS0ujr8uSTT/Ld736XW265hblz5/Lb3/6W1tZWXnvttdN+vU8Guf1actLog/CWTTQIT597MoZdHVo2Zn7WfCxGMTui+2NsM2diTBFLQP0dwwwP+DGaDGQn8Me0VWkZiPxEQmYCf8yFVlYCSEkyMzsvhYMtbrbV9nDL/DGlMt0nU/Gi9jqXrRbuuyRnCZubN7OzfSf3zL5HiDmWLqPnmV8yvFNr0R6budKzXL2tHkaG/NjHZK7ScifjSEvH09dLW3WlIBYVkwFLUTK+6n58tW7M2eKyzZKSEjo7O6mrq6O8vPwTvjISyYVF0OfjqS9/7rw89rd+87+Ybbazcm673U5PT4LSdoTXX3+dNWvW8PnPf55NmzYxefJkvvGNb/DXf/3XgPYBsb29ndWrR38/paSksHTpUrZt28Zdd911Vq4bZEZGcpK09I/Q2DuM0aCwuDjGHxMakylIMAhvov1K0bJSQn9MPwA5pcmYzDH+mKpjhAIBnGnppOWKCyZDg36CnSOggKVkfH/M+RyElwjdJ7O1+tTmySzN1bIuezr2EAiL04Ht8+ehmM0EOzsJxHQS2V0W0vM0EdJa1S/EFEWJihe9hDcWXSDGLpAE6ZORSC4U3nzzTZxOZ/T4/Oc/H3cbVVV5//33Wb9+PatWrRr3XLW1tTzzzDNMnTqV9evXc9999/Gtb32L3/zmN4A2dgEgOztbuF92dnY0draQGRnJSaFP850zOQWnNebHpn0/+AbAmgI5YukorIYnFjI7JxIy47ddN0beXPMTzY+JvLmacxwYHWLpqLu7m6GhIYxGI5Mniwbh883y0gx+vbk2mvkS0A2/bRXgdYNtVKBNS5tGijUFt8/N4e7DwvoHg82Gfd48hnfvxrNzJ5aYLNTkqan0tnpoOd5P6YIsIVYwaw7HPt5E85GJhEx/XKanqKgIRVHo6elhYGCA5OTkuPtLJBcrJquVb/3mf8/bY58KK1eu5Jlnnon+3eEYzZ7qIicQCBAOh/niF7/IunXr2LJlC2vXro3e7le/+hV333034XCYRYsW8eMf/xiABQsWcOjQIX75y1/y5S9/+RM+s0+GFDKSk0I3+iZuu9bnx1wOMUsbq/qq6Pf1YzfZKZ8klhkCHZ346+vBYCBpUQJ/zARG3+bo/JjTKysVFBRcMP4YncXF6ZgMCo29wzT3DZOfljQaTJkM6aXQW6MtkZw++ovGoBhYnL2Y9xvfZ2f7zrg9VklLljC8ezfDO3eR9oUvCLG8aWkc3NQS17kEoyW71qpjBP1+TGPm7VjydZ9MkGDnsFBestvt5OTk0NbWRn19PXPnxs/4kUguVhRFOWvlnTONw+GgrKwsYUwXORaLhby8PEwmTQ4sWrSIioqK6O30DEtubi6zZs0SzjFz5kz++Mc/ApCTkwNAR0cHubm50dt0dHQwf/78M/WUEnJeS0ubN2/m5ptvJi8vD0VR4gxBidrHrr/++vNzsZ9yTmpR5ARlpQVZCzAbROGgZ2NsM2dijPnU3tc+zMiAH6PZQHaxKEgCfh9tVdp+pQkH4SUw+l6oZSUAp9XE3HztuW49xXUF0Xky7QkMv5Fsl+6TGUve1FQAelo0n8xY0nLzcKSlEwoEaKsWO6J0nwyMvt7CpcrykkRyQaOLnMLCwqiIAe2DSFlZWfRwubS5UStWrKCyUvw9cPz4cYqKigDt/3xOTg4bNmyIxgcGBtixYwfLly8/q8/lvAoZj8fDvHnzePrpp8e9TWz72P/8z/+cwyuUADT1DtPcN4LJoLCoKKbMEwqM+mNOcX7MhGWlSLdSzpQUjGbxx7Tt+DFCwSDO9AxSs3OFWGjAR7Bb88dYJ/DHXEhG37HoQjFuMB6MCsW6+Hkyelt7RWcF/pAoSCbyySQlnwmfTH9cTAoZieTS4qGHHmL79u38+Mc/prq6mt/97nf8+te/5v777we03xcPPvggP/zhD3n99dc5ePAgX/rSl8jLy+PWW289q9d2XoXM2rVr+eEPf8htt9027m1i28fS0uL9EpKzi/6mOjc/BUesP6ZtP/iHwJYK2bOFUFgNs6djD3ACf8wE82Pyp6fGxSbcrxTJDpjznBjs4rV2dXXh8XgwmUwXnD9GZ/mUSYDmSYprtSyKdIR1HIJhcVN2aWop6bZ0fCEfB7rEIXa6TwZGhw+OZXIkK6O/5mPRhcyEPpk6N2pYvNbCwkIURaG/v5/+/vjzSiSSi4vFixfz6quv8j//8z/Mnj2bH/zgBzz55JPcfffd0dv8wz/8Aw888ABf+9rXWLx4MUNDQ7zzzjvYznIp7oLvWtq4cSNZWVlMnz6d++67b8L2MACfz8fAwIBwSD4Z2ybyx+hljuIrwCD+OFX2VjLgH8BhdjArQ6ytBjo68Dc0aP6YRPuVqrSMTF6i/UpjjL6x6GPzJ/LHxKZSLyQWFqVhNiq0ur009oozWnBlw6TpgDq6ZTyCoijRrJfe7j6W0fJSfEx/jU/GJzOWWJ/MWKxWa1QsyqyMRHLuef7558ed3zJRbCJuuukmDh48iNfr5ejRo9HWax1FUXjsscdob2/H6/Xy/vvvM23atNO4+lPjghYy119/Pb/97W/ZsGEDP/3pT9m0aRNr164lFAqNe5+f/OQnpKSkRI8LZXLrxYqqquyojQzCS+SPmWC/kl5WuizrMkwGUThE/TGzZmGM1GB1ets8jAwGMJkNZBeL3pmAzxudH1OYyB9TN74/5kLarzQedouRBQWasEi4rmCC8pKe9dJ9SWOZyCejm6l7WjyMDJ6iTyby/UnUhq2/znIwnkQiOZtc0ELmrrvu4rOf/Sxz5szh1ltv5c0332TXrl1s3Lhx3Pt85zvfwe12R4+mpqZzd8GXIE29I7T0j2A2KixM6I+JTIxNNAjvZNquE5SVWqPzY1IwmgwxsWOEQ0GcGZNIyc4RYkF9I7MBrDECKBwOX/D+GJ1lE/lkJti7pGdk9nfuxxfyCbEzM08mfu/SRD6ZsULmVCeSSiQSyclyQQuZWKZMmcKkSZOorq4e9zZWq5Xk5GThkJw++pvpvPxUkiwx5ZiWvRAYBns6ZImlo2A4GPXHJFwUuWOCRZGV48+PaZ7QH9MPgHmyC4Mt3h8zMjKC2Wy+YP0xOsumaAMHt9Um8MnogrHzCHjEvUzFycVk2jPxh/3s7xR3JJ3QJxN5rSfyyTQl9MmkAuP7ZAwGA263m76++LKVRCKRnAkuKiHT3NxMT0+P0KMuObtM6I+pn9gfMxQYwmV2MSN9hhALtLURaGwEoxF7rD8mrNISyQok2q/UeHii+TGRslICf4xeViosLIzuGrlQuawwDYvJQMeAj7rumMWLjkmjolGf3xNhrE8mYRv2Um0CcCKfjF5eapnAJ9NWVRnvk5nsHNcnY7FYoqJRlpckEsnZ4rwKmaGhISoqKqLDd+rq6qioqKCxsZGhoSH+/u//nu3bt1NfX8+GDRu45ZZbKCsrY82aNefzsj81qKoazcgk9sdE3kgn8McszF6IMWZIXnS/Unk5RqdTiPW2efAOBTBZDGQVid6ZgNdLe/VxAApmJZof06+d9yLZrzQeNrORywpTASae8jtBeWlCn8yOHePOk+ltPYFPJjK/R+dEPhm9DVsKGYlEcrY4r0Jm9+7dLFiwgAULFgDw8MMPs2DBAh599FGMRiMHDhzgs5/9LNOmTePee+9l4cKFbNmyBespjmmWnB4NPcO0ub1YjAYuK4zJjgR90LhD+zrBILyJ5sd4dmj3cyyJj+kZgdyy1HH9Ma6MTFKyxH0ewV4voX4fGBQsMQP0xvpjLsRBeInQ27ATGn718tIEht8D3QcYCY4IMfv8eSgWC8GuLm2i8tjYyfpkTrCuIO5SI8Kxrq5O+mQkEslZ4bz2oF5zzTUT/nJbv379ObwaSSx6NmZ+QSp2S0w5pmUPBEfAkQmZMaWjcIC9HXuB0YmzY9FLGxMtiky0lqDpiGY2LSgf3x9jyXdisIrX2tHRgdfrxWKxXDRlSd0ns722N26XkSZkFOiuhKFOcI7uSCpwFZCdlE3HcAcVnRUszxudqGmwWrW9S7t2MbxzF9YYUTd5Wtro3qXLEu9dGt8n0xD1ySiG0WstKCjAaDQyODhIb28vGRkJMnsSiUTyCbioPDKSc0vUHzNhWekKiBEVR3qOMBwcJtmSzLQ0cYZAoLWVQFOT5o+5LIE/ZoJFkfr8mIIE82Oi/pgJ1hJcDP4YnfmFqVhNBrqHfNR0DYnBpPTR4YMx5SVFUU66DTuWs+GTMZvN5OfnA3KejEQiOTucspDxeDwnvpHkomesP0bPDgiMHYQXg/4Guih7EQZF/BHTO2Zss8sxOh1CrKfVg88TxGQ1kpnIH1MT8cfEGH1VVT2pRZEXS1kJwGoysqj49ObJTGj4PYm9S5/IJxPJjI1FzpORSCRnk1MWMtnZ2Xz1q1/lo48+OvGNJRctdd0eOgZ8WEwJ/DEBLzRHPu0XXxV33+j8mAnKShO1XeeVpmA0ij+aLcePEg6FSM7MIiVLnB8T6vUScvvBqEQXGepcTPNjYlke6RRLbPiNCMj6+P+H+ut+uPswwwExQ3L2fTITD8aTPhmJRHKmOWUh88ILL9Db28uqVauYNm0a//zP/0xra+vZuDbJeUR/81xQkIrNHOuP2Q1BLzizYdJUIRQIBdjXuQ84jUWRelkpQdu1PoxtorKSJd+FIcbL097ejs/ni+7supjQW951n4xA0eWAAj1VMNAmhCY7JzPZOZmgGmRv514hpvtkYLw27AnmyZSf3jyZ/Px8jEYjQ0NDdHd3x91XIpGcee655x4URYk7qqurhZjFYqGsrIzHHnuMYDA47vmKi4sTnk9fGgng9Xq5//77ycjIwOl0cscdd9DR0XHWn+spC5lbb72V1157jZaWFr7+9a/zu9/9jqKiIm666SZeeeWVCV8IycXD9onWEujljAT+mEM9hxgJjpBmTaMstUyIBVpaCDQ3g9FI0mWXCTE1rEazAHkJjb7j71eaaH6Mno0pKiq6aPwxOnPzU7GbjfR6/BzviPHJ2NMgN9KCniArsyh7EXDi8lIsE/lkdBHZdvwYAb84OTjqkxlO7JPRV4XI8pJEcu64/vrraWtrEw69xK7HqqqqeOSRR1i3bh0///nPxz3Xrl27hPO89957AHz+85+P3uahhx7ijTfe4OWXX2bTpk20trZy++23n90nyScw+2ZmZvLwww9z4MABHn/8cd5//30+97nPkZeXx6OPPsrw8PCJTyK5IFFVNerLSDwIb4L5MW3am+OinET+GC0DYJ89G4ND9Md0twzhGw5ithrJKhT9MX7vCB01VUB8RuZE/piLYb/SeFhMhjE+mQSZjOg8mc1xIb28tKvt1PYuTeSTSc3Jw5mWTigYpO34BHuXEvhk5DwZyaWCqqqE/aHzcpxqaVbPRI899A90eqyoqIj77ruP1atX8/rrr497rszMTOE8b775JqWlpVx99dUAuN1unn32WR5//HFWrVrFwoULee6559i6dSvbt28//Rf8JDjt9uuOjg5+85vf8Pzzz9PQ0MDnPvc57r33Xpqbm/npT3/K9u3beffdd8/ktUrOETVdHrqHfFhNBuYXpIrBwAg0Rz7JJxAyuj/mlMtKlaPzYwwx/pjWY0ci/pjsuPkxE/ljQqEQjY2N2qVehEIGtIzYlqputtX2cM+KGLNy8ZWw7ReJfTKRzqUjvUcY8g/htIwOHoz1yYxtw9Z9Mr2tHlqrxDZsRVHIH9OGXThbHEponZKKr6ofX60b5wpxDUSsTya2fV4iuVhQA2FaH916Xh4777HLUWJHYZwh7HY7PT0J/HgJ8Pv9vPDCCzz88MPR/8t79uwhEAiwevXq6O1mzJhBYWEh27ZtY9myZWfluuE0MjKvvPIKN998MwUFBfzud7/jG9/4Bi0tLbzwwgusXLmSv/qrv+JPf/rThIsdJRc2uj/mssK0eH9M8y4I+cGVCxmlQsgf8lPRVQGMsygyMghPH5U/luj8mOmpcbGmIyduu7YUXFr+GB09I7ajrpdwONYnsxwUA/TWgrtFCOU4cihwFRBWw6fuk5l+Yp9M80SG3wQ+mcmTJ2MymfB4PHR1dY33dCUSyRnkzTffxOl0Ro+xZSAdVVV5//33Wb9+PatWrTqp87722mv09/dzzz33RP+tvb0di8VCamqqcNvs7Gza29s/ydM4IaeckfnKV77CXXfdxccff8zixfGfugHy8vL4v//3/37ii5OcH05uLUG8P+ZA1wF8IR8ZtgympEwRYv7mFgKtrWAykbRgvhALh1XaqvuBcebH6ELmFPcrjfXHGAwX58ikOZNTcFiM9A8HONY+yKy8MVknWwrkzofWvdo8mXl3CfddkrOEpsEmdrbt5Kp8sbssacmSyGC8naTd+QUhNnlaKgc/bJ7YJ1Ol+WTMltEp25Z80SdjzhktH5pMJgoLC6mtraWuro6srKy4c0skFwOK2UDeY5eft8c+FVauXMkzzzwT/btjTElfFzmBQIBwOMwXv/hF1q1bx5YtW1i7dm30dr/61a+4++67hfM+++yzrF27lry8vNN8JmeWUxYybW1tJCUlTXgbu93OP/3TP532RUnOH6qqsmPCRZFjhEwMY8tKsaUDPRuTyB/T06z5Yyw2I5kF4u4l/8gw7afpj7kY58fEYjYaWFySzsbKLrbV9ohCBrR5Mq17NQN2jJBZnLOYP1b9cXzD79NPR30yY79fsT4Zu8sSjek+maG+XtqOVwrlJcWo+WR8Vf34avoFIQNaeam2tpb6+nqWJsjKSSQXA4qinLXyzpnG4XBQVlaWMKaLHIvFQl5eHiaTJgcWLVoU3X8IWkZlLA0NDbz//vu88sorwr/n5OTg9/vp7+8XsjIdHR1nPSN+yh9TXS4XnZ2dcf/e09Nz0XWFSOKp7hyie8iPzWxgXkGMOAiMjJkfc2r7laL+mIRlpYg/Zmq8P6al8ihqOExKVjbJmeKneMEfU3jp+WN0ovNkEu5dOvECyWO9x3D7xPkuE86TcVrImKyJkNjyku6TgRO0YZ9gnkw4HI5/LhKJ5Jyhi5zCwsKoiAEtEVFWVhY9XC6x+eK5554jKyuLG2+8Ufj3hQsXYjab2bBhQ/TfKisraWxsZPny5ZxNTlnIjOea9vl8WCyWhDHJxYPuj1lUlI7VNJ4/Jg/SxdKRN+hlf9d+IN4fo6oqnl260TfBosiI0Xfy1PHLShO1XU/kj7HZbHGfKC42Rn0yPYRifTKFy0AxQn8D9DcKoaykLIqTi1FR2dOxR4idyCeTFynxtSYqL30Cn4zZbGZkZCThhyGJRHJhEw6Hee655/jyl78siB+AlJQU7r33Xh5++GE+/PBD9uzZw1e+8hWWL19+Vo2+cAqlpaeeegrQPpH953/+J07naAkgFAqxefNmZsyYMd7dJRcJE64lmMAfs79rP4FwgEx7JkXJRUIs0NxMsLUNzGaSIpvOdcJj5sckNPpGBuEVls+Ni13q/hid8rxkXFYTg94gR9sGmD15zPO1umDyZZrIrNsCC8Ra9uKcxdQP1LOrfRerCkUj30n5ZGIm/MLJ+2QCHcNYckfLS0ajkcLCQmpqaqivr79oDdgSyaeV999/n8bGRr761a8mjD/xxBMYDAbuuOMOfD4fa9as4d///d/P+nWdtJB54oknAO3T9S9/+UuhjGSxWCguLuaXv/zlmb9CyTkjHFajg/AS+mPGDsKLYWxZKc4fEykr2efMwRDjr+puGsTvDWGxm5hUIKYwfcPDdNRWA5A/a7YQO1l/zMVeVgIwGQ0sKUlnw7FOttX0iEIGtO9H8y5NaMYImSU5S3j5+MvjL5A8CZ/M8ICfpOTT8MnU9gtCBjS/Uk1NDXV1dWf9U5pE8mnm+eefP63YRFx33XUTzrKx2Ww8/fTTPP3006d1/tPlpD+q1tXVUVdXx9VXX83+/fujf6+rq6OyspL169dLA99FTlXnEL0eP3azkbn5qWLQP6ytJoCEQmZ3uxZL2Ha9c6KyUj+gvXEaDKIAaq08ovljsnNInnRq/piGhgbtUi8BIQOjHWSJ9y6N8cnE/JJZlKNN+K3sq6Tf2y/ETtYnk2jv0qhP5kDc5ZyMT6ahoUH6ZCQSyRnhlHPuH374IWlp8V4GycWPXlZaVJyGxRTzozGBP2YkOMKBbu0NLaE/ZocmZBIuitT3KyVYS9AY3a80fllpPH+M3++/JPwxOnqGbGddL8FQjAAoXAYGM7iboK9eCE2yT6I0RZv3s7tjtxAzWK3Y588HTt8n03R4fJ+MP4FPJjc3F4vFgtfrPSc7WCQSyaXPSZWWHn74YX7wgx/gcDh4+OGHJ7zt448/fkYuTHLuObm1BPH+mH2d+wiGg+Q4csh35QuxQFMTwfZ2MJuxx/pjQmFaJ5gf0/wpnh8Ty8zcZJJtJga8QQ63DjBv7MRliwMmL4Sm7VpWJl1sN1+cs5gadw0723eyumi1EEtasoThnTtP2yfTXl1JwOfFbLWNXs4JfDJFRUVUVVVRV1dHbm7uab4iEolEonFSv+X37dtHIBCIfj3eMbb3XHJxEQ6r7Kj7ZPNjluQsGd8fM3cuBrtdiHU1DRHwhrAmmcjIF+fHaP6YGuD058dcKmUlAKNBYemUicpLke9LgnUFeht2Yp+MFjvR3qXhgQn2LlXF7F0yjtm7FPk+CZc6pg1bIpFIPiknlZH58MMPE34tuXQ43jlI33CAJIuRufkx4uAE/hjd6KtvXB6LXlaaqO06tyzeH9Ny7DCqGiY1JxdXxiQh9mnzx+gsn5LBe0c62FbTw9evFtdDUHIlbPkXzZCtqkLWTBcy1f3V9Iz0kGEfFar2eRPsXYr4ZHpatL1LZQsn2rs0T7ic6N6lGjeucfYu6T6ZSyVrJpFIzg+n/BvkhRdekJutL0H0stKi4nTMxnH8McmT4/wxw4FhDncfBkY3LuuoqhrNyDgm2K+UP32C+TEzT31+zKXmj9HRM2W76nsJxPpkCpaC0QKDrdrupTGk2dKYmjYVOH2fTMJ1BVGfTCLDb8QnU5/YJ2O1WvH5fLS1tSV8rhKJRHKynLKQeeihh8jKyuKLX/wib731FqFQ6Gxcl+QcM/H8mDFt1zGlo72dewmpISY7JzPZKX7yDjQ2EuzoQDGbo8PXdEKhcHS/Ul4Co+/o/Bjpj9GZkeMiLcnMsD/EgeaYjiCzHfIjWa+6zXH31U3Y47ZhM1oGHItuwk68QFIzYbdVHSfg8woxS74TxRLxybR7hJjBYKCoSJs1JMtLEonkk3LKv+nb2tp46aWXUBSFL3zhC+Tm5nL//fezdev5WWsu+eRo/pgJ5sdM4I+ZaC2BJ7JfyTYvgT+mcZCAT/PHTJos+mO8niE667SsQn659MfoGAwKS0u078/20/TJJN67NL5PZvLUNFCgry2BTyY7F2d6BuFQkNbKY0JM88lEpvwmaMPW91/V1dXFPw+JRCI5BU5ZyJhMJm666SZefPFFOjs7eeKJJ6ivr2flypWUlpae+ASSC45j7YP0DwdwWIzMiR225h+G5vH9MbvaRo2+seilCseSBGWliD8mb2oqyjj+mLTcPFzp0h8zFn2eTGIhM8E8mexFKCjUuevoGu4SYrE+mbHYnGYyIkIztrykKEo0K5N471JEyNT0x19q5PvT2Ngos7oSieQT8Yly70lJSaxZs4a1a9cydepUmSa+SBmdH5PIH7MTwgHNH5MmtvUO+gc50nsEiM/IqKoa3XidlHB+TD8AkxP5YyKzSU51fkxbW9sl64/R0TNmu+v78AdjfDL5i8FohaEO6K4SQinWFGakaytEYstLgk9mx6mWl8b3ydhKtfv56gbifDLZ2dnYbDb8fr/0yUgkkk/EaQmZ4eFhXnzxRW644QYmT57Mk08+yW233cbhw4fP9PVJzgGj/phTmx+zt2MvYTVMgauAHIe4N8dfV0+wqwvFYsG+YL4QG+uPSWj0jQiZ2LISjH66/7T5Y3SmZTvJcFgYCYTY39wvBs02KIiIxvp4n4w+5TdxeUn3yeyIi+kzfvQs2lh0sdlecxy/d0S8nDwnitWI6g0SaIv3yehZGVlekkjOPPfccw+KosQd1dXVQsxisVBWVsZjjz1GMBgc93yhUIjvfe97lJSUYLfbKS0t5Qc/+IFQjlZVlUcffZTc3FzsdjurV6+mqqpq3HOeKU75t/1dd91FVlYWDz30EFOmTGHjxo1UV1fzgx/8QC6NvAgR/TEnWBQZw9j5MbFE58fMn4/BahVinfWDBP1hbA4z6TG7eLxDQ3Q2aP6YxPNjTmz0vVTLSqCVc3TBub1movJSvE9mIsOvY6kW8+zclXiejAL9HcN4+n1CLCUrG9ekTMKhEK2VR8VrNSpY9XkyE5SXZCZXIjk7XH/99bS1tQmH7k/TY1VVVTzyyCOsW7eOn//85+Oe66c//SnPPPMMv/jFLzh69Cg//elP+dnPfsa//du/RW/zs5/9jKeeeopf/vKX7NixA4fDwZo1a/B6veOe90xwykLGaDTyhz/8gba2Nn7xi1+wfPnys3FdknPE0fYB3COn54+ZyOirf7JPWFaqHF1LEOuPaT56CFSVtLx8nGmisAr1eAkNjO+PaWxs1C71EhYyAMsm2rtUMkbIxAiShdkLMSgGGgcbafe0CzHbvHkoViuh7m78tWL7ts1hJjOy0LOlKt4no28mT9iGrZeXJti7JH0ykosJVVXx+/3n5ZhoYWMirFYrOTk5wqEvfNZjRUVF3HfffaxevZrXX3993HNt3bqVW265hRtvvJHi4mI+97nPcd1117Ez8qFVVVWefPJJvvvd73LLLbcwd+5cfvvb39La2sprr7122q/3yXDS2691XnzxxbNxHZLzhL7tenFJOqZYf0zTjog/Jj/OH+P2uTnWq3WqJNyvpBt9l06wX2mC+TETtV1/Wv0xOssjGZk9DX34giGspjGvxeSFYLKDpwu6jkHWzGjIZXExM30mh3sOs6t9FzeX3hyNGSwW7AsWMLx9O54dO7DGGPfzpqXS1ThIS2U/0xaLZcT8WXM4vGnDhHuXfHVu1JCKYhwVrllZWdjtdkZGRmhpaaGwsPD0XxSJ5BwRCAT48Y9/fF4e+x//8R+xWCwnvuFpYLfb6elJ8OEowuWXX86vf/1rjh8/zrRp09i/fz8fffRRdC1RXV0d7e3trF49ugYlJSWFpUuXsm3bNu66666zct1wkkLmqaee4mtf+xo2m42nnnpqwtt+61vfOiMXJjk3nO5+pT0de1BRKU4uJjMpU4j5a2sJdXejWK3YYufHBMK012iCZKL5MXo3zFhOpu36UvbH6JRmOsh0Weka9LGvsV/83pmsmk+mbpP2/RsjZEATnYmEDGiic3j7doZ37CT9i18UYvnT0tj/flNCn4yekWmvrcI/MozFnhSNmfOcKDYjqjdEoG0IS74rGtN9MkePHqW+vl4KGYnkDPPmm2/idI6Ot1i7di0vv/yycBtVVdmwYQPr16/ngQceGPdc3/72txkYGGDGjBkYjUZCoRA/+tGPuPvuuwFtGCkQ90EyOzs7GjtbnJSQeeKJJ7j77rux2Ww88cQT495OURQpZC4iQmGVnZH9SstPc7/SRPNj7AsWYIj59NBRP0AwEMbuivfHjAwN0tVYD0D+zNlCTPTHpMZf6qfAH6Oj+2Te2N/K9tqeeBFacqUmZOo2w5K/FkKLcxbz3OHnEht+I9OXh3fuRA2HUcYIwtypqSgKuLtGGOrz4kwbXRKZnJlFSlY27s4OWo4doWTB6KoKxaBgLUnBe7QXX41bEDKgzZM5evQodXV1XHXVVaf9mkgk5wqz2cw//uM/nrfHPhVWrlzJM888E/27wzH6O1cXOYFAgHA4zBe/+EXWrVvHli1bWLt2bfR2v/rVr7j77rv5wx/+wIsvvsjvfvc7ysvLqaio4MEHHyQvL48vf/nLn/zJfQJOSsiM7SqQHQaXDkfbBhjwBnFaTZTniZ4T/B5o2aN9PYE/JqHRd4e+lmCCstK0tLgFk81HDoKqkpFfiCNVLDuJ/hjxzfDT5I/RWR4RMttqenhwdUywOCII6j+CcBjGCJLLsi/DqBhpGWqhdaiVPGdeNGafPRvFbifU14evqhrb9GnRmNVuIrPQRWfDIC3H+5m+VCwvFZTPxd35Ho2HDwhCBjTh6T3ai6+2H9fV4nZ0/fvV1NREMBjEZDrlardEck7RO30uBhwOB2VlZQljusixWCzk5eVF/+8tWrRIWACtZ1j+/u//nm9/+9vREtGcOXNoaGjgJz/5CV/+8pfJydF+J3R0dAhb7Ts6OpgfGe9wtjjlHPxjjz2WcNfSyMgIjz322Bm5KMm5QW+7XlyclsAfs3OMP6ZYCPV7+znedxwYbenVGbtfKSnhfqUT+2PyZ0l/zInQB+Pta+zHG4gxyk6+DMxJMNILnUeEkMPsoDyjHIhvw1YsFpIuuwwgOgNIOO2Ebdja96x5osF4dQOoIdGsmJmZicPhIBgM0tLSkvjJSiSSM44ucgoLC4UPEHa7nbKysujhcmkfHIeHh+PK9kajkXBYm2dVUlJCTk4OGzZsiMYHBgbYsWPHWW8KOmUh8/3vf5+hoaG4fx8eHub73//+GbkoyblBFzL6m6LABP4YffFgaUopk+zi5F1fVRWhvj4Uux37bLE8FAyEaK8ZAEaHrI1FN4smNvr2A9Ifo1OckUR2shV/KMzehhhhYTRD4TLt6wnWFUy4d2lXgsF40ydaIKn5ZDpqa/ANizNjzLkOFLsJ1R/C3zIoxBRFkfNkJJKLgJtvvpkf/ehH/PnPf6a+vp5XX32Vxx9/nNtuuw3Q/i8/+OCD/PCHP+T111/n4MGDfOlLXyIvL49bb731rF7bKf/WV1U1riQAsH//ftLTE8whkVyQhM7SfiW9rJS0YAFKrD+mdoBQMExSsoXU7CTxfgNuunV/zITzY1LjLzUiZPT5CJ8GFEWJ+poStmGPXVcQw9h5MrHtnHo5cHjnLtSwODk4tywFxaAw0O1loEccfufKmERqTi6qGqb5qDgYU/fJwMRt2HKejERy4fJv//ZvfO5zn+Mb3/gGM2fO5O/+7u/4m7/5G37wgx9Eb/MP//APPPDAA3zta19j8eLFDA0N8c4772Cz2SY48yfnpAvSaWlp0UmA06ZNE8RMKBRiaGiIr3/962flIiVnniOtAwx6g7isJmblnpo/JjoIL3f8QXgnKivF+WOOHgJgUkERScli1kX6YxKzvDSD1ypaE+9dKhnfJzM/az4mg4k2TxvNQ80UuAqiMVt5OYakJEJuN77KSmwzR7ueLDYTWUUuOuoGaD3eT/JycRFowaw59Le30XTkIKULxZ8N65QUvEd6NCFzTYEQG+uTCQQCp2xolEgk8Tz//POnFRsPl8vFk08+yZNPPjnubRRF4bHHHjvnNpOTFjJPPvkkqqry1a9+le9///ukpIy+2VgsFoqLi+VwvIsI/c1vyUTzY1IK4vwx3SPdVPdXA9oiwrGo4XBUyCQ2+vYDE5eVPok/JisrK8EzvXRZPkUr61U09TPiD2Ef+9rkzgeLE7z90HEQckfb4JPMScyZNId9nfvY1b5LEDKK2Yx90UI8m7fg2bFDEDKg+WQ66gZoqexjxvJcIVZQPpeDH7w74WA8f70bNRRGGfMzN2nSJJxOJ0NDQzQ3N3+qMmsSieSTc9JCRm+vKikpYcWKFbK74CLndPcr7W7X/DHT06aTZhMNu77jxwm53ShJSdjKy4VY0B+ivU4TJLppdCz6m1/hac6PKS4u/tT4Y3QK0u3kpdhodXvZ3dDLlVPHzPMxmqBwOVS/p30/c8V5PotzFrOvcx8723dy+9TbhZhj6VI8m7cwvGMnGffcI8QmT09l7/qGxAskIyK0s74W79AQtjHzK8zZSRiSTISHg/ibh7AWjWYBdZ/MoUOHqK+vl0JGIpGcEqf8m9/j8QiuZJ3169fz9ttvn5GLkpxdgqEwO8+KPyaylmDhQpSY8kB7rZtwUMWRaiUlSyxJDLv76WnWykOTZ4oCSM6PGR9FUUbXFSTau6SvK6ibwCfTFu+TSVoSmSezezdqzOqA3NJUDAaFwV4vA92iT8aZnkFa7mRQVZqPTeST6Y+/1Ih4kYZfiURyqpyykPn2t7+dcC+Kqqp8+9vfPiMXJTm7HGkbYNAXxGUzMes058cszY33wEy8lqAf0D7Rx/pjmo5o/pjMwmLpjzlFdMNvQp+Mbvht2Aph8f/svMx5mA1mOkc6aRhoEGK2mTMwOJ2EBwfxHj0mxMxWI1mRRZDNidqwIx1nE+5dqhnf8NvS0oLf749/LhKJRDIOpyxkqqqqmDVrVty/z5gxg+rq6jNyUZKzi/6mt7QkHWPM0kbNHxPU/DGpRUKo3dNOw0ADBsXAwuyFQkwNhRjepQmZiRdFTjA/5hPsV/q0+WN09IzagWY3Hl9QDObOA2sy+NzQLgoLm8nG3EytjBc3T8ZkImmR5n9KOE9meiowcRt2QiETKQ36GwZQg2JHVHp6Oi6Xi1AoRHNzc8LnKpGcL051WaPk5DkTr+0pC5mUlBRqY7bjAlRXVwvjjyUXLqe7X0nvVpqVPguXRcyOeI8dIzwwgMHhwBYjdAO+EB31+vyY8f0xifYreSfwx+hliE+jP0anID2J/DQ7wbDKrvpeMWgwQtHl2tcTlJd039NY9K4zz85EQkb7HrYe74/7JaT7ZLoa6xkZEmfGmLKTMDjMqIEw/ub4eTKyvCS50NA76BINgZWcGfTX9pN0K56yY/eWW27hwQcf5NVXX6U0siG3urqaRx55hM9+9rOnfSGSc0MwFGZXvfZJOqGQ0d/wEpSVdrRpb2qJ264j2ZhFi1BijODtNW7CIRVnupXkSeI8AU9/H70tTaAoifcr1fQDo2WJsXya/TFjWT4lg5f3NLOttodrpsdkpoqvhOPvaPNkVoh70BbnLOaZ/c+ws31n3HwovTw4snsPajAofE9zpqRgMCkM9flwd42QmjU6E8iRmkb65AJ6W5poPnKQqUsuj8YURcE6JYWRg934atxYi0VxWlxczIEDB+Q8GckFg9FoJDU1lc7OTgCSkpISzlGTnDqqqjI8PExnZyepqakYjcYT32kcTlnI/OxnP+P6669nxowZ5Odre1Oam5u58sor+Zd/+ZfTvhDJueFw6wBDviDJNhMzY+fH+AZH/TEl4gI/VVVH/TE58f6YqNE3QVmpeYL9SnpZKbOoBLtTzPIEu0YIDwbAZMBaKF5rMBikoUHzdnzau1yWl2pCZvtEht+GbRAKat1MEeZlzsNqtNLj7aHOXceU1CnRmHXGDAwpKYTdbryHD2Mfs8XcbDGSXZxMW7Wblso+QciAllnrbWmi6bAoZIBRIVPbD9eK267176Puk7lY9tlILm30HUK6mJGcWVJTU6Ov8elyykImJSWFrVu38t5777F//37sdjtz586Vm2svErZF58dkxPtjGraBGtJmx6SKbzLNQ820edowGUzMz5ovxNRgkOHdWnki4SC8Cfwx+m6egkTzY/RsTHEyilksHbW0tBAMBklKSvrU+mN09MzawRY3g94ALtuYFG32bLClgNcNbfshf9TbZDFamJ85nx3tO9jZvlMQMorBQNKiRQxt2IBnx05ByIBWXmqrdtNyvJ/yKycLscLyOex/989RkTqWqOG3YRA1GEYxjX5fU1NTSUlJwe1209jYOO6yO4nkXKIoCrm5uWRlZREIBM735VxSmM3mT5SJ0TmtYTCKonDddddx3XXXfeILkJxbRufHJFgnUbdJ+7MkXpTubNOyMXMnzSXJLH4C9x49RnhoCIPLhW3mDCHm9wbpbND8ELpJdCyNkUF4ifwxvup+AKyl4/tjSkpKPvWp3rxUO0UZSTT0DLOrvpdVM8YszjQYoegKqPwz1G8WhMz/z955h8dRXm/7nu2rVe+SrWI1y7Ll3gsYm47poYQAISQE0klIL6R8EFJISPklISTUJITeTLGxjW1w702ybPXeu7TaPt8fs7vSu7s2mNhg4/e+Ll1aONrVrDTWnDnnOc8BbelnIJG5sfhGIWabN5ehdeu0atsX7xBi44sS2PVGHc1He8PaUgFTw66GOuwD/cIkmiHFii7aiG/IjathUNA+Bfxk9u/fT11dnUxkJKcVer3+pFx0JSefD6WQHB4e5s033+Thhx/mT3/6k/AhOX1xeUb9YxbmJ4d/QTCROTcstL3tePoYf1tp9myUkH/ordX9qD6V2GQLsUmif8xQbw+9LU2aPqY4xD/Gp+II+MdE0McEBOdne1spQHDv0of0k9nVtgufKk4SBapr9j17UEPuRNPyYtEbdNj7XfS1i0LIqNg4krO0ibfQbdgBnQxIPxmJRHJyOOGKzN69e7n00kux2+0MDw+TmJhIV1dXsMT/9a9//f1fRPKxsL+pD7vLS6LNRHG6qEfB3gNt/otOJH2MvyITuPCNZTigj4nkH+NvK2UeZ+w6NTdPcIEFcLcOo454UMx6TOPEY3W5XMERXZnIaCzIT+KZnY3HXyDZsA28bm07tp/S5FKsBiu9zl6q+6opTCgMxsyFhejj4/H29TFy8BBRM2cEYwajnvS8WJqP9tF8tI+EdHFiMWvyVLoa62ksP0jRfFE4bs6PZ+RAF47qfmLPDzlUv3C7paUFp9OJ2Wz+ED8NiURyNnHCFZlvfvObXH755fT29mK1Wtm2bRv19fXMmjVLin1Pc7ZUaRe5BXlJ6EL1MYEtySmTIFrUnNT219Lt6MasNwe9RwKoHg8juzSBsC3iosg+AMZH2q90aD9wjLZSQB8zIQ5FLx5rQ0MDPp+PuLg4uXHdT0AnU9YyQP9ISB8/tQSsieAehpa9QsioNzI9ZToQwU9GpwuKt+3HGcOO6CdTEjDGi6CTCfjJNA6gusUqUHx8PPHx8aiqGjQ7lEgkkuNxwonMvn37uOeee9DpdOj1epxOJ1lZWfzmN7/hhz/84ak4RslJYnN1FwALCyKNXb+rfY6gjwm0laanTsesF++QHWVl+Ox2dHFxmCdOFGKuEQ+dDZo+JlJFpsGfyORMmRYWGx27lvqYD0JarIW8ZBuqSrB9GESng9xF2uPA73kMgXZhwCdoLIEq23AkY7yiQCIT7iczvmQKKArdTQ0M94mJjiHZii7GBB4VZ8NA2OvK9pJEIjkRTjiRMRqNQfOx1NTU4F1TXFwcjY2NJ/foJCcNu8vD3gbtgrIokj6m5v2FvpHGroe3+dtKc2ajhJjStVT1afqYFCsxiaJ/TH9HG/0d7ej0+vD9Sl4fzlrtAhdJHzM2kZGMcty9S7n+32tduE4msDdrV3u4Tsbmr8iM7NmLL2R1QFpuLHqjjpEBF72tok7GGhNLSnYuAE2HDwkxRVGCCWogYRUO1d9ekn4yEonkg3DCicyMGTPY6beiP/fcc7n33nv5z3/+w913382UKVPe59mSj4tddb24vSqZcRZyksSpIwZaoLsSlDF37n58qo+d7drvO9KiyOFtWwGwzV8QFmuq0BKn8RPDqzH1B7VqTHrBREwWUQTsahpCdXnRRRkwhmgvRkZGaG1tBaQRXihBwW8knUxA8NuwHTxiQlKSVEKUIYp+Zz9He48KMVNBAfqkJFSnE8f+/UJMb9SR4U9Ijt9eCl9XYPEvAA2soBhL4Pfa2tqKw+EIfy8SiUQyhhNOZH75y1+SkZEBwP33309CQgJf+tKX6Ozs5JFHHjnpByg5OYy2lZLD2zGBaZaMaWAVk46jvUfpd/YTZYhicrJYOfE5nYzs0TQXtgXzw75nMJEpPnZbKft4baW8OJQQLU99fT2qqpKUlERcXHjb6WwmoJOpaBugzx6yeDGlGKKSwTMyanrox6gzMiNNE/IGqm8BFEUhaq6WwA5vF2Mw2l6KuEDS/7sN/K7HEqjIuBoG8TnFhZYB7ZOqqkHTQ4lEIjkWJ5zIzJ49m/POOw/QWkurVq1iYGCA3bt3M21a+EVJcnoQaDcszD9BfYx/LcGstFkYdeIujJG9e1GdTgwpKZjy8oSYfcBFd/MQEF6RUVU1eJd+fH1MfPihyrbSMUmJMVOQGo2qwraaEJ2MooyunYjQXgpMo0XSydjmaUlqoPo2lkCS2nykF58vdO/SFBRFR29rCwNdoiuqIcmKPtECPhVn7bGrMrK9JJFI3o+zc9PeWUa/3c3BZu1isaggRB+jqsc1wgtc2CKOXW/dBkDUgvlhVZ7A2HXS+GisMaLVfFdjPfb+PgxmMxlFokBYdftw1kt9zIcl0F7adrz2UiTBr//3u7t9N16fWCGxLdTahiP7D+AbHhZiqTkxmCx6nHYPXY3iIkhzlI30Am2cu+FgeFXGEnD59RsfCocqBb8SieQDIhOZs4CtNd2oKuSn2EiLFUW39NZCfyPoDJAt6lw8Pg+72rXVA5GM8Ia3+vUxCxaGxZoqtIpAxLaS/6I2vngyeoNY5XHWD4BHRRdrwpASYqA3NBTcdyL1MZFZ8EEEv007wS1qT4oTi4k2RjPoHqSit0KIGbOyMGZmgtuNfbfYltLpdcGJtEArcSzZU6YDx2gvFby/4LetrY2RkZHw9yKRSCR+ZCJzFrA1oI+J6ObrvzsfPwdMorC2vLucYfcwsaZYJiaIlRPvwACOQ9o0SkR9zJFjC30byt5fH2PJjw+r8gTaDGlpadhsNiThzJug+eocaR+ke8gpBpMLIToNPA5o3iWEDDoDs9K09QU7W8X2kqIoRPmrMoEq3FgCyWogeR1LTumoTiZ0RDtQcXO3DuMdEjU9MTExJCVpSZnUyUgkkuMhE5mzgM3+u/NFx/WPCV9LEDBIm502G71OXD1g37kTfD5MubkYQzaX9neOMNDlQKdTyCyMF2I+r5emci0BOq7Q9338YySRSYo2MzFNc0LeHuonM1YnE2FdQWAqLdQYD8C2IJDIhOtksoq15Kmlqh+PW2xLZRRNwmAyM9zXS3eTaHCnjzYFp9Kc1eE6GdlekkgkHwSZyHzCaR9wUNUxhKKMTrUEUdXjCn2DawkitpVG9TGhBO7M0/JiMVnELRht1ZW4RuxYbNGk5IoJic/pwdWk6SykPubDc/z2kl8ncxzB756OPXh8HiFmm6/9np0VFXh6xAQpISOKqDgTXrePthrR4M5gNDKuuASAhoP7wr6nuSBee90IOhkp+JVIJB+ED5XIrFu3jhUrVpCfn09+fj4rVqxg7dq1J/vYJCeBwMVscmYs8VGi6JaOwzDcCQYrjJ8thFxeF3s7tNHqiEZ4QX1MBP+Y47WVxqwl0IVUeZy1A+ADfaIFQ4Ko5enr66OnpwdFUcjJyTnm+5WMJqzH3bvUtBPcovakKKGIGFMMw+5hyrvLhZghKSno3GzfJraXFEUZbS8dDm8vBSpv9RF1MvEAOI6jk2lvb8dut4fFJRKJBD5EIvPXv/6Viy++mJiYGL7xjW/wjW98g9jYWC699FL+8pe/nIpjlPwPbK7S9DER3XwD1Zjs+WAQVw/s79yPw+sg0ZJIfny+EHO3d+CqrgZFCTq/BlB96qh/zKTwPUgfxD/GEqEaE7grz8zMxGKxhMUlo8zPS0RRoKpjiI7BEEO5pHyIyQCvCxrFFpJep2d2mpbQftj2UiQ/mZzS6Vqs/CA+r9h6Mk+IA52Ct8eBp1tMrKKjo0lJSQFkVUYikRybD2WI99BDD/Hf//6Xr3/963z961/n6aef5qGHHuKXv/zlqThGyYdEVVW2+CsyC47nH5MXro/Z2qJdrOZnhI9W27drd+SWkhL08fFCrLtlCMeQG4NZT1purBBzu5y0HD0MQHap1MecKuKjTExK13722yP6ybx/e2lX266wWEDUfTzBb0fdAE67uLQyJXcClugYXCMjtFWLzsE6sx5TtqbpOV5VRiYyEonkWJxwItPX18fFF18c9v8vvPBC+vvDBXuSj4+GHjvNfSMY9QpzJ4RUR3xeqNukPT6OEd6CzPDWUeBCdjw338yCePSGkN1LFYfxut1EJyaRkDFOiHmHXLhbNI+SUH2MqqrU1NRohyoTmQ/EcdtLQT+ZYwt+93Tswe0TE5Ko2bPBYMDd1IQrZK9adIKF+LQoVHV043kAnU5P1mRtXUEkPxmz9JORSCT/AyecyFxxxRW8/PLLYf//1VdfZcWKFSfloCQnh81V2kVsRlYCUSZRdEvLXnD2gzkO0sXqSL+zn0Pd2mTR/AwxWVFVNdhaiIqwX6nxsJbIZE2KpI/ZB2htpdAqT2BqxZgehT7EQK+zs5PBwUH0ej3Z2dnHfL+SUQIVuG0RBb/+yaXm3eASDe4KEwqJN8cz4hmhrKtMiOlsNqx+9+5I7aXRMexjt5fq/efAWCz+yTZntbZkVHieXw/V2dnJ0NBQ+HuRSCRnPSecyJSUlHD//fdz2WWXcd9993HfffexYsUK7r//fqZMmcKf/vSn4Mf78e6773L55ZeTmZmJoii88sorQlxVVe69914yMjKwWq2cf/75VFZWnughn7Vs8fvHRGwrVa/XPk9YAnoxydnVpm1Bzo3NJd0mjla76urwtLWhGI1EzZopxLweHy3+u+qIRnj+tQSR9DGOSu3iZy4If16gGpOTk4PRaAyLS8KZOyERnQI1XcO09ocYyiVMgLhs8LmhfosQ0im6/3kMO5KfTOB33nq0AnfIIkjT+BgUkw7fsAd3m5hY2Ww2UlNTAdlekkgkkTnhRObRRx8lISGB8vJyHn30UR599FHKysqIj4/n0Ucf5aGHHuKhhx7iD3/4w/u+1vDwMNOmTTumSPg3v/kNf/rTn3j44YfZvn07NpuNiy66SG7E/QD4fGpwYilsLQFAjT+RyT8vLLS1VbtIRWorBSZWrDNmoLOKzrvtdQN4nF4s0UaSMqOFmGN4iPbqKiA8kVFVNdhWsIT4zgBUV1cDkBeyz0lybOKsRqaOjwfgvcouMagokL9UexxIaMdwXMGv3xjPvm07qs8nxDKL4lEU6G2zM9QrmvHFp2cSk5yC1+OhuUKs9CgGnSb6JbLLb+D3HkhoJRKJZCwnnMjU1tZ+oI8P8kfnkksu4b777uPqq68Oi6mqyh/+8Ad+/OMfc+WVVzJ16lSeeuopWlpawio3knAOtw3QPewiyqRnela8GHQOjU6s5IUnMttatWQltK0EH0wfM35iQtjW6qbyQ6iqj4SMccQkiYmVp2sEb58T9AqmCaLQ1+v1Bu/E8/PF6SnJ8VlSqP2cN4UmMjD6e68JT2QCgt99HftwesWExFpaii4qCm9vL84jR4SYxWYkxS/cbToiVmUURflAY9iRdDKB33t1dXWYO7BEIpGctoZ4tbW1tLW1cf755wf/X1xcHPPmzWNrhLJ2AKfTycDAgPBxNhK4eM3PS8IUIrqlfrPWVojPhkSxytEy1EL9QD16RR9sMQRQvV6Gt2si4Kj5xzbCi9hWOt7Ytf/iZc6JRWcSvWWamppwu91ERUWRlpZ2rLcricBifyVuc1VX2GZq8pYCCnSUw2CbEMqPzyfZmozT62Rfxz4hphiNRM3RzovhLZF0MoH20rF1MscV/Nb2o3rESk9OTg56vZ7+/n66uyNofiQSyVnNCScyqqry/PPP8+Uvf5lPfepTXHPNNcLHyaKtTfvjGnrxSktLC8Yi8cADDxAXFxf8yMrKOmnHdCYRaCcE7soFAu2EvPO0NsMYAtWYKclTiDHFCDHH4Qp8/f2a6LO0VIi5HB7a/a6ugYvZWOr9rq6Rxq4dlX0AmN+nraTTnbZ592nJjOwEokx6uoddlLeGJPRRiZDh/13UbBBCiqKwIENrIW1pETU0MNpeGt4WYQx70qgxXmj1JJDEdtTXYB8QJxyN6TZ0NiOqy4crZIu2yWQK/juW7SWJRBLKCV8Z7r77bm655RZqa2uJjo4Wkoa4uHD/j4+aH/zgB/T39wc/GkPGRM8GHG4vO+q06kjEROZ4+piW4+lj/NNKc+eiGESBcGtVPz6fSkyShbiQrdUDXZ30NDeiKLpwfYxXHTXCO47QV+pjThyTQRccw95UFaG9lL9M+1z9Tlgo8PsPnA9jCUyr2XftQnWJyx4z8uLQG3QM97voaxfdeG3xCSSNzwZVpbHsoBBTdErQP8jxPu0liUQiGYvh/b9E5F//+hcvvfQSl1566ak4niDp/kWE7e3tZGRkBP9/e3s706dPP+bzzGYzZrP5mPGzgR21Pbg8PjLiLOSniKJbBlqgswJQwhZF+lRf0D8moj5mS2AtQXis0d9WyorQVqo/qK06SC8swmITj8fVNIjq9KJYDRjHibGRkRGam5sBqY/5sCwpTOadig42VXZx17khP8P882DT77WKjKoK1blAInO45zA9jh4SLaNVNnNRIfqkJLzd3Yzs3x9sNQEYTHoyCuJoquil8XAvCenilvKc0ul0NzXQcGgfExcsFmLmgnhGDnRpie0F4hqKvLw81q1bR21tLV6vF71ebEFKJJKzlxOuyMTFxX0kd8cTJkwgPT2ddevWBf/fwMAA27dvZ0GE/T6SUd6r7AS0i1ioX0uwjZA5XWsvjOFIzxF6nb1EGaKYmjJViPkcDuy7NLdX28KFYd8zsGMnYltpv5bI5E6dERZz+seuLQXxYQLhuro6VFUlKSnptKj2nYkEKnI76npwhGymJmseGKNgqF3Tyowh2ZrMxARtt9K2lvDdSscbwx71k4kwhu1vLUbSyQQqcq6GQXxOcWllwILB5XIFk1uJRCKBD5HI/OxnP+PnP/85IyMj7//F78PQ0BD79u1j3759gCbw3bdvHw0NDSiKwt133819993Ha6+9xsGDB7n11lvJzMzkqquu+p+/9yeZgD5mcWFKeHCsPiaEgD5mdvpsjDrRr8W+U2sjGNLTMYVUR4b7nHQ3D4MCWSH7lXw+b1AfkzNV9J2B0TZCYGpFOFR/G0FWYz48+SnRpMdacHl87KgNSSwMZsjxJ6URxrCD7aXW8GQluK7gOILf5iO9+LyicHf8pFJ0ej197a30tYtaN0OiBX2SBXxq0CAxgE6nC95AyfaSRCIZywknMtdffz29vb2kpqZSWlrKzJkzhY8TYdeuXcyYMYMZM7Q79W9961vMmDGDe++9F4Dvfve7fO1rX+OLX/wic+bMYWhoiFWrVsmlgcehY8BBRdsgijI6tRJEVUcrMsfTx2REWEuweTMAtkULw6o8DeXaBTI1OwZLtJgAddTW4BgaxGSNIqOgSIj5nB5cDZqw01Io9TGnAkVRWBwYwz6eTibCGHYgkdnSsiVMuBuoyo0cPIg3ZDIwJTsGs82Ay+GlvVaMmaOiyCgsBqBu/56w7xk4DxxHw6eepJ+MRCKJxAlrZD772c+ye/dubr75ZtLS0sJbFyfA0qVLj+sLoSgKv/jFL/jFL37xob/H2UbgYjUlM45Em2j1T3sZDHdo7YSseULI6XWyp0O7sETUx2zW9jJFL14cFmv0t5WySiK0lQ5obaXsKdPQheganNX94FPRJ1kwJIrJaW9vLz09PSiKElwcKPlwLClM5oXdTeHGeDBamavbDB6nsAV9ZupMzHozHfYOavprhC3oxowMTPn5uKqrGd66jdiLLgzGdDqFrOJEqnZ30FDeQ0ZItS132kyaK8qoP7CH6ReKWjtLUQLD21qDLcexBCpzTU1NOBwOeUMjkUiAD5HIvPHGG6xevZrFES5oko+fTcG20nGmlXIWChcsgL0de3F6naRYU4QLFoC7vR1nZRWM0UYEUH1qMJHJLglfhVB3QEuOcqdF0McE3HwjtJUCd93jx4+XF6z/kYCz8+HWAToHnaTEjPndp06C6HQYaoOGbcImdIvBwqy0WWxp2cKWli1h50X04kX0VFczvGmTkMiAltQGEpl5V4gVtdxpM9n87L9oOLQfr8eDfswEnDkvDnQKnm4Hnu4RDEmjE3Dx8fEkJSXR3d1NbW0tkyZN+p9/NhKJ5MznhFtLWVlZxMbGnopjkfyPqKrKe1Uf0D8mhEBbaX7G/LAq2/Amra1kKS1FHx8vxDobB3EMuTFa9KTlieeFa8ROy5EK4Fj6mGPvV5JrCU4eydFmSjK0383m0PaSovjN8YjYXlqYqbWQIo1h2/w3M0ObN4V7xvircx31AziGxC3aaRPyscTE4hoZobWyQojpLAZMOZp/kSNCVUa2lyQSSSgnnMj87ne/47vf/a5c4HYacqR9kM5BJ1ajnlk5IcmB2zG6IDCCPmZzs5asLBwXPpEU0MdEL14UFgvoY8ZPTECvF0+nxvJD+Lwe4tMyiE8Tl096+p14OkZAAUu+OJHk8/mora3VDlUKfU8KgcQ2YnspcD5EEPwG2oy72nfh8oqeMVGzZ6OYTHhaWnGFJBbRCRYSM22gjo7mB1B0uqDLb51/om0slqKATqYv/FCln4xEIgnhhBOZm2++mfXr15Ofn09MTAyJiYnCh+Tj472j2kVqXl4iZkOIz0bjdvCMQHQapJYIoU57J0d6j6CgBO/AA6heL8NbtATItig8kWksD7SVjq2PyTnO2LVxfAy6KFEg3NraysjICCaTiXHjxh3z/Uo+OEv8E2ybqjrDdWmBikzrfrCLSUdRQhFJliRGPCNh6wp0VitRs7UFk8ObNoV9z4BmKpDsjiV3mlahO57g11ndhxoy9ZSbm4uiKPT09NDbG16xkUgkZx8nrJH5IFutJR8Po22lSGPXfvfWvKVhawkCNvQlSSWC8RmAo/ww3r4+dNHRWKeK3jIuh4c2/5hsJKFvXSCRiaCPcRzx+8cUhbeVqqq0LdkTJkyQxmcnidm5CZgNOtoHnFR1DFGYNmb9REw6pE6GjjKtvTTl2mBIUbTkdmXNSra0bGFuxlzhdW2LFzO8ZQtDmzaT+NnPCrHskkT2r22ksawbVVWFlmXAU6i9tgr7QD9RsaNVOWNmNDqbAd+wNtVmHrNI1GKxMH78eBobG6mpqWHWrFkn5ecjkUjOXD7U1JLk9MPh9rK9Rluod04kfUzVWu1zwflhoUBbadG48IpLYFopav48FKNYOWk+0ovPpxKbYiUuJUqIDXR20NvShKLTkT1ZTIBUrxrcr2SZeOxEprCwMPx9SD4UFqOeuRMSea+yi41HO8VEBrT2UkcZVL0jJDKgjWGvrFnJ1tat3M3dQsy2eBH8Buw7d+JzOtGNcdXOLIhHb9TWFfS0DJM0xrk5OjGJ5OxcuhrqaDi4j+JFoyJjRadgLkhgZH8njqO9QiIDWnupsbGR6upqmchIJJITby01NDQc90Py8bCrrhenx0darJmC1NC1BK3QfghQIH+5EPL6vGxp1Soyi8eFT6IFhL6Rxq4bjtNWClRjMgqLMUeJNvWupkFUhwfFasA0XrygjoyM0NTUBEBBQcGx3q7kQ3COv1K38WhneDCQ4Fat1fyGxhBcV9CtrSsYi7mwEENaGuoY5+cABpOecf5FoIHJtrGMtpeOo5M5zhh2TU0NXq83LC6RSM4uTjiRyc3NZcKECcf8kHw8bDzaAWhtpTBvn0A1ZtxMsIkj0mXdZfQ7+4kxxlCaLG609g4NYfe7Lh9PHxPq5guj+phIawkcR7TnWQrD1xLU1NSgqirJycnEh0xISf43lk7UEpnttT2MuEISgJyF/nUFbf6kd5RkazJFCUWoqMFdXAEURQmeG4GkdyzH1cn4J9nqD+wJ0+0EdDLu5iG8w+LU07hx47BarTgcjmDSK5FIzl5OOJHZu3cve/bsCX5s376dhx9+mKKiIp5//vlTcYySD8CGI9pdduBiJVC1RvtccEFYKNBWmp85H4NO7DTad+wAjwdjTjamrCwh1t85Qn/nCDqdwviQ9pDP56UhuJYgQiLjd221TAxPgGRb6dRRkBrNuHgrLo+PrTUh00sGM0w4R3tcuSbsuYsytWRlU3O4qDcwzRZJ8BvwFmqp7MMTkjyNKy7BYDIz1NtDV2O9ENPHmjCma1NPziqxKqPT6YJVmcD5IpFIzl5OOJGZNm2a8DF79mzuuOMOHnzwQf70pz+dimOUvA9NvXYqO4bQ6xSWFIQkMl4PVG/QHkfQx2xq0S4+kdtKfjffSNUYf6sgLS8Wk1VMgNqqKnEMD2G22UjPFxMS75ALd9MQEL6WQFXV4IVJtpVOPoqicK4/0Q0kvgJj20shBM6PTc2b8KniJFHUggWgKDgrK3G3twuxhIwoohPMeN0+Wvy6qAAGk4nxJVOAyNNL5uOMYQcS3crKyvD3IZFIzipOOJE5FhMnTmTnzp0n6+UkJ0DgojQzO564kFFmmnaCsx+siVpraQx9jj4OdWlthNCxa4ChwH6lSPqYMk1YHMnNt3afppXImTozbC1BQORrzLChjxVXKLS3tzM4OIjRaCQ7Ozvie5X8bywtGk1kwsawC/0Vu4Zt4BCXNs5InYHNaKPH0cPh7sNCzJCQgKVUa0uGtpcURfmA7aUIOhm/vsZR2Rt2rIGKTFtbG4ODgxHfq0QiOTs44URmYGBA+Ojv76eiooIf//jHsh3wMTHaVkoNDwbaSvnLQCcmFdtat+FTfRTEF5BuEw3rXA0NuOsbwGAgaq44cuv1+mj2j09nTw5vD9Xu1RKZvBmzw2LOgD7mOG2l3NxcjCETUpKTw8KCZIx6hYYeO7Vdw2IwIReSCkH1ji4X9WPUG4PmeO81vxf2usH20uZjt5eO5yfTdPgQbqdDiJlz41CMOnwDLjztdvH7RUeTmZkJyPaSRHK2c8KJTHx8PAkJCcGPxMRESkpK2Lp1K3/7299OxTFKjoPT42VLtaZ3iKiPCegdIrWVmo/dVhra+C4AUTNnoo8Wp6DaqvtxObxYoo0kZ4lTR8N9vbTX+BOSaWIFSPWpwSmU4/nHyLbSqSPabGBOrpZERmwvBaoyEXQyS8YtASInMqPrCraghkwSjS9OQFGgt3WYwR4xWUkcN56YpBS8bjdNh8uEmGLUabuXiLwNO3CeyPaSRHJ2c8KJzPr163nnnXeCHxs2bKC8vJzq6moWhCwUlJx6dtb2Ynd5SY0Z3acTZLAd2g5ojwvEsWtVVdnccmz/mKGNGwGIPvfcsFj9Qa2tlDM5CV3I1FHtvt0ApOUVYosXkxV38xC+YQ+KWR/cpxPA6XQGx/dlZe/UEkh4N0Qcw/afJ1XrwsawA+fJwc6D9DrExMI6dSq6mBh8/f04Dh4UYhabkdRc7dwMtCQDKIpC7nQt4Q1U8oTnBnQyFeHVnMB5IsewJZKzmxNOZM4991zhY8mSJRQXF2MwnLC3nuQksP6INna9dGKEsevqddrnjOkQLbadjvYepWukC6vBysxUsXLis9u1iSUgeml4IlN3yJ/IlEbQx/gvRhMitJWC00oF8Sghe5lqamrw+Xxy1cVHQKAFua2mO8IY9mIwWGGwBTrKhVC6LT04hh1wgw6gGAzYFmo6q6F33w37njlTtHOl/lB3WCxvxhwAavbsCB/DLtbOBWfdAD6HR4jJMWyJRAIfIpHp7h79Q9TY2Mi9997Ld77zHd6N8MdLcurZEExkIuhjPkBbaW76XEx6UXQ7vG07qsuFcdw4TCHbpwe6RuhtHUbRKWH+MV6PJyjajKSPCSQy5uO4+cq20qmnMDWazDgLLo+PbTUhiYXRAhO0FtKJtpcC1buhDRvDYrmlmtt0Y0UvXrc49ZRdOg29wUB/Rzs9zWJCYkiyYkixgk8Nay/JMWyJRAInkMgcPHiQ3NxcUlNTKS4uZt++fcyZM4eHHnqIRx55hGXLlvHKK6+cwkOVhNLYY6e6cxi9TmFRQchaAp93dL9SYQT/mA/YVgqt8gTuqNPzYrHYQpY9Hq3AaR/GGhNLWr6YkPjsblwNA0C4PkaOXX+0aGPYWuIbSIQFAn5DxxnD3ty8Ga9PrOZEn6MlOY7yctwd4usmZ0UTFWfC4/TSHOLWa7JYGV+iTT3V7A2ffAxUZSK1l6RORiKRfOBE5rvf/S6lpaW8++67LF26lBUrVnDZZZfR399Pb28vd955J7/61a9O5bFKQghchGblJBBnDZnyad4Njj6wxME4sToy5Bpib7tWOQkYnQVQVXVMInNO2PcMJDKBO+yx1PjHrnOnz0IXMiHlqOoDFQypURjiLUKsq6uL/v5+9Ho9ubm5x37DkpPGcXUyhf4KXsNWcAwIoWmp04gxxtDn7KOsWxTnGpKTsfgXiw6HVGgVRRltLx2M0F6aqbWXavccJ5E52ovqE1tPgURGjmFLJGcvHziR2blzJ/fffz+LFi3iwQcfpKWlhS9/+cvodDp0Oh1f+9rXqKioOJXHKglh/fHcfCvHjF3rRf3SlpYteFQPubG5ZMeKfi3Oo5V42tpQLBai5s0TYm6Xlyb/2HXgojSW4+pjAtuuI7SVAnfTubm5mEymsLjk5LPIP4Zd3x1hDDsxDxLzweeBWrFNZNQZmZ95nDFsf/I7uGFDWCyQ/NYd6g7TwgR0Ms1HynHaxeMx58aimPX4hty4m4fE7xcdTUZGBiDbSxLJ2coHTmR6enpIT9e8RqKjo7HZbCQkjF6UEhIS5B3RR4jDPTp2fd7x/GMi6GM2NmkXp3PHhwt5A9UY27x56Cxi5aT5iKZviE4wk5gpLoIc6Oqkq6EORdGF7VdSferofqXjJDKyrfTREW02MDsnMIYdqb3kP2+Op5NpipTILAVgeMtWfC6XEBtfnIDOoDDQOUJfiC9MfHoGCZnj8Xm9YUskFb0uaI43cpzpJZnISCRnJyck9g3VS4RNyUg+MrbX9uBw+0iPtVCcLo4yM9gOLf6LQUgi4/V5g0Lfc7OOk8hEaisFxq5Lk8N+93X+seuMwolYY8QxcFfTIL4hN4pZjzk3Tog5HA7q67U9O0VFRcd8v5KTz9LjrSsoHKOTCameBHQyZd1ldI2IO5ssJZMwpKSg2u3YQ5y+TRZDcBt2XaT2kr+SV/shdTLV1dVyDFsiOQs5oUTmtttu45prruGaa67B4XBw1113Bf/79ttvP1XHKIlA4C763KIIY9eVq7XPmTMhRnTsPdR9iB5HDzHGGKanThdi3v5+RvZqCVBMiH+Mqqqj+pgIbaWa47WV/BcfS1ECikE85aqrq/H5fCQlJZGUFP66klPHecVaJW9rTTd2lzjaTK5/DHugGdpEX5iUqBQmJU4CCB/D1umCSXCk6aWcKVp7qf5QV1gsqJPZtxvVJ042BZyg3c1DeAdCKj3jx8sxbInkLOYDJzKf/exnSU1NJS4ujri4OG6++WYyMzOD/52amsqtt956Ko9V4kdVVdZXjPrHhHFklfZ54iVhoY2N2sVl0bhFGHWiQHho0ybw+TAXFmAcN06I9fhdWfVGHeOKxfaQx+0ObruOmMj4F0wG7qqFQz1yBJDVmI+DwtRoshK1bdjvVYYkFkYr5J+nPT66Kuy5wSWSTRG2YQfGsDduDNPCBLyHWiv7cY6E+MIUl2CyWrH399FWI04h6WNMGMdrDtOBNmUAnU4XrMoEzieJRHL28IFd7B5//PFTeRySE6C6c5i6bjsmvY4lRSGJjNsBNeu1x0UXhT333SZtmuSc8eGto2Bb6Zxjt5XGFSVgNIkTSc2Hy3A7HdgSEknNFX1nPH1O3K3DoIQnMj6fL6iPmThx4rHeruQUoSgKy4vTeGJLHesOt3PRZLF6R9HFcORNOPIWnPtdIbRk/BL+cfAfbGrZhNvnFpJi24KFKEYj7oYGXLV1mPMmBGPxqVHEp0XR126nsbyHglmj+i69wUjO1BlUbt9CzZ5dZBSI54RlYiLupiEcFT3Y5ojHOnHiRA4ePMjRo0e58MIL/9cfjUQiOYM4aduvJR8daw+3AzA/P4loc0guWvsuuO0QOw7SpwqhtuE2jvQeQUEJ26+ker0Mv6fdXUdcSxBw843YVtI0DbnTZoa1uRwV2vNM2bHoQ3xnmpqaGBkZwWKxkJWVddz3LDk1nD8pDYB3KjrwhYw2BxPhlj0w2CaEpiZPJcGcwKBrMDjKH0AfbSNqjtYmCiTHYwlUZSK2l/zTS5F0MtaATqaqD9Ujtp4KCgrQ6XR0dXUJpp0SieSTj0xkzkDWlmuJzAWTIkwrBdoARRdBSFIRqMZMS5lGgkVsDzkOHsTb24suJoaoGeLUkWPYTWt1PwC5IWsJVFWletc2APJni+PaMKatNOnYbaWCggL0en1YXHLqmTshkRizga4hF/ua+sRgTLqmswI4uloI6XX6YFVvfeP6sNcNrLYYijSGPWZdQagvTKA12V5TxVCv2EIyjotGF21EdXpx1vULMYvFQk5ODiDbSxLJ2YZMZM4wuoec7GnQPFmW+e+mg6jq6AWnKII+JjB2HWFaaTDQVlq0CMUoVk4aD/eg+lQS0qOITbaKx9NYT39HO3qjkdxSMQHyubw4qvuA0bvpsRw9ehSQbaWPE5NBxzl+ndU6f6VPIKCziqCTOS9L09Csb1wfpoWJXroUAPvu3XhDbBkyCuIxWvSMDLrpaBBjtvgE0vK0cerQJZKKTgmKfh0V4duwA+dR4LySSCRnBzKROcNYf6QTnwolGbGMixeTCtoOwkATGKNggqhzGfGMsL11O3AMfcw6bZ1BzHlLw2J1B7QWQE4EN9/q3Tv8sekYQ3xnnFV94FHRJ5gxpEUJsd7eXjo7O1EURfrHfMyc76/srTscwU+m6GLtc/V6cI8IoQWZCzDpTDQPNVPdVy3ETNnZmCZMAI+H4c3iZJPeoCPbX6GrPxhpekmrytREcvmdeOxt2AHBeH19PSMjI2FxiUTyyUQmMmcYgbbS+SVp4cHAXXPeedryvzHsaN2B0+skw5ZBYXyhEHM1NuI8ehT0+jB9jNfrC+pjJkwLT2SqPkhbqTgxTDsTKP9nZ2djtVrDniv56FhalIpOgYq2QZp6RaM60kshdjx4RjT91RiijFHMy9B+7xuaNoS9bnB6aX146ymQFNceiJTIzAWg/sBe3C6nELMUJYBewdM1grtDPNbExERSUlJQVVXuXpJIziJkInMG4XB7ebdSMy+7ILStBNp0CUScVgroGM4Zf05YUjG4bh0AUbNmoY+PF2ItlX047R6sMUbS80Qzu6HeHtqqtDJ+4OITQPWpQRdW66RwgbBsK50+JNhMQZffsKqMooyeT4HzawxLs5YCsKFxQ1gsepnWehrauBHVI45a55YmoSjQ1TjEYI9DiKXlFRCdmITb6aDh4H4hprMYMOfHAzBSHi7qle0lieTsQyYyZxDbarqxu7ykxZqZMk50z9XcfPdoj0MSGa/PG0xklmUvC3vdYFvp/OVhsdr92h1zbmkyOp2YANX420rpBUVEJ4gaGHfLEL5BF4pJjzkv3M23rq5OO1TpH3NacH6J1l5aG1Enc6n2+ejqMJffwJqLA50Hwlx+o2bORB8fj7evD/uePULMGmMioyAegNr9orOwoigUzNH2OQWE5MJzS7TE2FEWnsgEzqfKykrp8iuRnCXIROYMInCRWT4pLdzNN9BWiuDme6DrQNDNd07aHCHm6e3FvltbLxC9TExkVFWldp92kZkwPdx4r3q3prkpmD0/LDbiv8hYiuLD3Hyrqqrw+XwkJiaSnBzerpJ89Cz3V/i21XQz6HCLwdzFYLTBYAu0ihWSNFsak5Mmo6KG7V5SDAaiz/NXZfxVv7EEWpU1+8LbSwWzFwCaBsvnExMSa4mWNLsaByO6/EZFReF0OmloaDjue5ZIJJ8MZCJzhqCqKmvLtbJ/xLZSxeva5+JLw0LvNGgVlyXjl2DUh7j5btiouflOnIhpvOjm29U4xFCvE4NJR1aIm6/b4aDe7+abP0tsKwGMlGkXJ+vk8ETl8OHDAEyaNCn8fUg+FvJTopmQbMPtVXn3aKjLr2XU5ffIm2HPDbSXIo1hB6p8g2vXhU02TZimJcctlX04hsXkaXzJFMxRNuz9fbQeFcep9bFmTFnafrGRw2JVRqfTBasycgxbIjk7kInMGUJZywBtAw6sRj0L8kM0J44BqNmgPS6+XAipqsq6Bu1ueHl2eOto6B0tFrM8PFbjL/lnTUrEEOLmW3dgD163m7i0dJKycoSYu9OOp2MEdEqYm6/H4wkKMYuLi4/zjiUfNRf4BeRvl7eFB4tXaJ8Pvx4WCoxhb23ZisMj6l1sCxeiWCy4m5txhiQWcSlWksZFo/rUsOklvcEQ9JSpitBesgTaS8fRyVRUVIQlTxKJ5JOHTGTOEN4u0y4u5xQlYzGGmMdVrQGvC5IKIUUUz1b1VdE42IhJZ2LRuEVCzOdwMLRpMwDRy8O1MwF9TF6kttIuTR+TP2teWJsr0FYy58ehs4rOwzU1NbhcLqKjoxkXss9J8vESWFHwzuEOnJ4QfUnRRaAzQEcZdIuj1kUJRWTYMnB4HWxrFZMOndWKbbF23g2uPU57aX+E9tIcrb1UtXNrWEJinexPZKr68DlEIXF+fj4Gg4G+vj7a2iIkZRKJ5BOFTGTOEN46pP1BvnhKenjw8Ert86QVYW6+gbbS/Mz52Iw2ITa8dSvqyAiGjAwsJSVCbKBrhO6mIRRl1FI+gM/npWbPaCITSkCEGamtVFFRAWjVGJ1Onn6nEzOy4kmNMTPo9LClOqTSEZUIuUu0xxViVUZRlGBVZm392rDXjVl+PjA6HTeWQJLcUNaNxyUmTxOmz0RvMNDX1kpPc6MQM6RYMSRbwaviOCqa45lMJgoLNYuBQBtTIpF8cpFXkjOAqo4hKjuGMOoVlhWH6GPcDqhcoz0OaSsBvNOoJTLLssIrLoELS8x554VVVQLVmIyCeKzRJiHWfLiMkcEBLLZoxhWLCZC334mrcRCU0emSAD6fL5jISH3M6YdOpwSrMoEKoMCkQHtpZVjo/BwtWdnQtAG3T9S7RC89F3Q6nIcP42pqEmLJWdFEJ5jxuHw0hrj1mqxRZJdOB6Bqp1jpURQl2F6KNIYdOL9kIiORfPKRicwZwGr/RWVhfjJxVlGsS+1GcA1pSyIzxRUBrUOtlHeXo6AEBZkBVK+XofUbgGOMXR/wTytFMME7ul1zas2fPR+9QWwdBS4qpqwY9LFiAtTY2IjdbsdisZCbm3vsNyz52BhNZNrxhi6RLF4BKNC0EwZahNDM1JkkWhLpd/azq01cLWBISCBqtqZ3CZ1eUhQlOBEXmJAbS2AMu2rn1rBYsL1U0RO2RLKwsBCdTkdnZyddXeFtK4lE8slBJjJnAKuO21Z6TftcfBmEtGoC1ZgZqTNIsorVkZG9e/F2d6OLjQ1eZIKxIRctldpSvsBkSQDV56Nqh5bIFM0XNTcwqo853rRSUVGRXBJ5mjIvL5E4q5HuYRe76kLWAMSkw3j/+H7FG0JIr9Mfv700ZnoplLxpoy6/oRu482fNA0WhrbqSwR4xITFlxWhLJB1enDXiEkmr1cqECRMAWZWRSD7pyETmNKexx87B5n50yuhUSRCvZ9RtdVJ4W2l9w7FN8AZWvw1AzLJlKCaxclK7rwvVp5KcFU1cirg+oKXyCEO9PULZP4DP7g5eUAJ3ywFUVRX0MZLTE6Nex/n+8f5VEdtL/vPsOO2ldQ3r8IZ4vwQ8iuy7d+PpFVtIGYXxmKMMOIbctFb1CTFbfAIZhZqAvWqHWJVRdEqwfRkY9xcOVbaXJJKzApnInOYE2kpzchNJjjaLwcZtYO8GayJkLxRCvY5edrVrJf5QfYzq8zG4WtuSHXPRhWHfs3qP5leTPzM1LFa5XZtyyps5B0PIluyRih7wqRjSojQh5hja2tro6+vDYDDIJZGnOYHK39tl7eHjywGdTN0msIsVm3np84gxxtDt6GZ/p2icZxo/DnPJJPD5GFwrVmz0eh0TpmpVmeo94e2lonla5e/ots1hMesU7Xkjh7pRveKxBhLmlpYW+vr6jvV2JRLJGY5MZE5zAm2lS443rTTxEtCLWpV1Devwql6KE4vJis0SYiP79uPp6EBns2FbJLaHHMNumvyiy4KQREZVVSo/UFspfLdSoBpTUFCAKaQCJDm9WFKYTJRJT3PfCIeaB8RgYh6kTQHVO+om7ceoNwa1WGvq14S9buxF2ibtwVWrw2L5s7RzrXpvB2pIeylwrjVVlDHcJ1ZzzPlx6KIM+IbdOGvF9lJ0dDTZ2dnA6PknkUg+echE5jSmY8DB7gbtD/dFoYmMqo6ak0VoK71dp7WOLsoNXyAZqMZEL1uGLrSttL8Tn08laVw08WlR4vHUVjPQ2YHBbCZ32kwh5nN6cBzRjjWSPqa8vByQbaUzAYtRz9KJmjZqVVlr+Bccp720PEdrIa1rCHfyjb1YOxeHt20Lay9lTUrEZDVg73fRWi0mJLHJqaQXFIGqUukXmgdQ9LrR6aVDsr0kkZyNyETmNGZ1eTuqCtOz4smIE1s12uRIE5iiIW+pEOp19LKjTfN5uTBHbB2pqsrA21qSE7iwjCVQ2s+fGW6Cd3TbJgDyps/GaLYIMcfhHvD4MCRbMWaKfjUdHR10dnai1+vltuszhMD00luH2iK0l/yJTNU6cA4KoUWZi7AarLQOaxNzYzHl5GCeNAm83vD2kkEXnJCr2hOygRsomr8YGD0HxxJVGmgvdYVVcwKJTENDA0NDQ8d8vxKJ5MxFJjKnMauPN61U9rL2eeKlYBSTnEBbaVLiJLJjs4WY48ABPK2t6KKiwtpKTrubxsOa7iFUHzO2rVQYoa1kP+DfrVSaHOZJU1ZWpr1mfj5WqzXsuZLTj2XFqZgMOmo6h6loE5MVUksgqQC8zlGxuR+LwcKScZpxXuT2kpY8R2ovBVqZNXsitJf8OpmmwxHaSwXxKFYDvqHw9lJ8fDyZmZmoqiqrMhLJJxSZyJymdA052VqjaU4unhySyPh8UPaK9njKNWHPDbSVLswNF/IGppWily5FZxGrKrUHuvB5VRIzbSRmiFWVrsZ6eltb0BuN5M0Qx7V9Dg+Oo1oCZJ0aMq6tqhw6dAiAyZMnH+vtSk4zYixGzvO3l14/IHrGoCgw2X/eHXop7LkX5FwAaInMCbeXLHqG+120hoxTx6WmkZ5fiKr6qAydXtLrRqeXDoa3lwLnXSChlkgknyxkInOa8tahNrw+lanj48hNFpMKGrfBYAuY4yBfnEh6v7bS6LRShLbSbv+00oxIbSVtYiR32kxMVlE7M3K4BzwqhhQrxnQx1t7eTnd3t2wrnYGsmJoJwMr9reHtpUACXbUWRsSEZMn4JVj0FhoGGzjcI1ZBTLm5wfZSqDme3qgL+hYFzsWxHK+9ZA20l8rC20uBRKauro7BwcGw50okkjMbmcicpqzcr90FX+6/mAgE7oKLLwODOJJ93LZSWTnu5mYUq5Xoc5YIMeeIh4ZAW2lWeFvpyJZ3gdGLyVhG/C7A1qkpx2wrFRYWYgmpAElOb5ZPSsVq1NPg9zISSJ2ktZh87jBzPJvRxjnjzwFgVa042QSj7aWBt8JjwemlSO2lwPRS+SHs/X1CzFIQj2Ix4Bt046oTJ63i4+MZP348IKsyEsknEZnInIa09o+w0++qetnUDDHo80L5q9rjCG2l1XVaxSVSW2lwtXbhiD73XHQhWpW6A134PCoJ6VFhbaWO2mp6W5sxGE0UzBaXRPpGPMGlfVFTxWklVVWDFw7ZVjrziDIZWDZJSyxePxBheuk47aVLJlwCwFt1b+FTxfUBx2svZY9pL7WFtZfSScsriNxeMuiwliQCYD8Y7kUzZcoUQCYyEsknEZnInIa8caAVVYU5uQlkxoeIY+s3w3AHWBPCppV6HD3sbNsJwEU5YutI9fnof0O7c440rVS1qx3QRL6hVZUKfzUmb9bc8LZSeTd4VQypURjTxASotbWVnp4eDAYDRUVFH+CdS043Lvcn0q/vbwlbHxBMpGs2wLC4uHHJ+CXYjDbahtvCzfHep72U+4Gml94LiwX0WZGml0r8290bGxulOZ5E8glDJjKnISv9d7+XTztOW2nS5aAXnXXHtpXCTPD27sXT0orOZiN66VIxNuSioUyrABXOEdcgqD5fMJEpXnRO2OEE2kqh1RgQ20pmszksLjn9WToxlWizgZZ+B3sbxeoJSfmQPlUzxwvs/PJj1ptZnq15yrxZ82bY6wbbS2++FRYrmKWdg1W7O8KSp4kLtESmsewQQ72is7AlML006MZZ0yd+v9hYcnJyAFmVkUg+achE5jSjodvO/sY+dApcWhrSVvJ6Ri8Yk68Oe27gghHJBK//dc08L+aCC8Kmlar3aCZ4yVnRYW2l5iPlDHV3YbJGMWF6yLSS3Y2jsg+IPK0UuGAEyvqSMw+LUR/c8bVyf4T2UqAqUxbeXro4V3Pyfbv+bTw+jxCLvVRrPQ1v24anU2wFZZckYo7SzPGaj4rJU1xqOhlFxaiqL6jbCqAYdEFPGXuETdqyvSSRfDKRicxpxkr/qOuiguTw3Uq1G7XdSlHJkCtWR1qHWoO7lS7Lu0yIqW43g35hZezlK8K+59Edml9N0dxwv5qKzdrFonDuQgwhLsAjh7qDu5WMqWLLqbm5mb6+PoxGI4WFhcd9z5LTmxX+9tKbB1vxhraXAgl13SYYEltB8zPnE2+OF1qeAUzZ2VinTQOfj4E3xYqN3qALin6P7mgPO55Ji5cCcHjThrBY1HR/e+lgF6pb1OZMmjQJRVFoaWmhu7s77LkSieTMRCYypxnHnVY6+IL2ueSKsN1Kb9ZqF4PZabNJt4kJydDmzXj7+tAnJ2ObJ4p1B7pHaK3qBwUKZ4ttJa/HExx1jdRWGt6rXbiiZoQvlzxw4AAAEydOlLuVznCWFKYQazHQMehkR63YziEhF8bNAnWMt5Efo84Y9JR5qza8hRR7heYQ3L/y9bDYxLnauVizpwOPW9ykPXHBEhSdjvaaKnpamoSYKTcOfZwZ1enVlpiOITo6mgkTJgCyKiORfJKQicxpxNH2QSraBjHqlaBFfBCXfbStNPWGsOe+UasJeVfkhVdcBl73i3wvuQTFICZAVbu0ZGRcUTzRCWIFqOHQfkYGB4iKiyd7yjQh5ul14PK7qEZNFxMZr9cbNMGbOnXqMd+v5MzAZNAF3aVf3dcc/gVTPqV9PvBsWCgwvbS2YS0ur0uIxV5yCej1OA4dwllTK8Qy8uOJTjTjcnipOyBWT6Ji44K7vkKrMopOwRqoyuwLFwsH2ksHDx4M98aRSCRnJDKROY0IXCTOLUohLkoU8lLxBriGtDvgLLGqcqTnCJW9ldodcO4FQsxntzPonwyJWyG2nGBMW2lOpLbSRi02fxE6vV6IBTQI5rw4DPFiAlRdXY3dbsdms5Gfn3+8tyw5Q7h6hubD8sbBVhwhFRJKPwWKHpp3QVelEJqZOpNUayqDrkE2N28WYobERGyLNW+YgdfFBZSKTgmek4FzdCyB9lLFpo1hCUkgsR6p6ME3ImpzSkpK0Ov1dHZ20toaQfMjkUjOOGQic5rg86m8vEdLZAIXDYEDz2ifp96gWcSP4Y0areJy7vhziTXFCrHBd9ajjoxgzM7GElId6W4eort5GJ1BIS/EzdftclK1U/PqKF54rhBTVRX7Xk27EKmttH+/Nm47ZcoU9CEJkOTMZN6ERMbFWxl0eFh7OES3Ep0KBdqEUmhVRq/Tc/EETfS7siZ8W3bc5VcAWnspNCEp8reX6g914xh2C7H82fMwmM30tbfSVnVUiBnTozCkRYFXDduIbbFYghvYA+epRCI5s5GJzGnCtppuWvodxFoMLJ8UkhwMtkH1O9rjkLaST/Udv620Urt4xK24LMwf5uhO7YKUMzkJi02sAFXv3IZrZISY5BQyi4qFmLtlGE/HCBh0QWv4AA6HgyNHjgAwbZrYjpKcueh0ClfPGAfAS3sitJcC5+X+Z7VdYGO4Il9LVjY0bqDfKZrcxSw7DyUqCndjIyP79gmxpHHRJI2LxudVqQ7xlDFZrBTMng9EaC8pSrAqY4/QXgqclwcPHsTr9YbFJRLJmYVMZE4TXtijiRZXTMvEYgypYhx8QRNTjp+reXeMYVfbLjrsHcSYYlgyXlw74OntZWizVs6PvSxkksmnUumfCIk0rVS2UWtHTT53OYpOPE3sfpGvtSQRnUXU3Bw+fBiPx0NycjIZGSHj45IzmqtnaonMxqOddA05xWDxZWCOhf4GaBBddycmTmRiwkTcPnfYygJdVBSxF5wPjCbdYwlUZSJOLy1ZCsCRre/hC0lIAtNLzpp+vP3isebn52Oz2bDb7VRXVx/vLUskkjMAmcicBgw7Paw6pOkArvVfLAQCbaVpxxb5XphzISa9OB00sHIleDxYJk/GHKJVaansY7DHgcmiJ7c0SYgN9nRRf2AfAJPPWS7EVK8avMsNFfnCaLl+6tSpYRUgyZlNfko007Li8fpUXtsXshHbaNWm6QD2/zfsuYGqzGvVr4XFYldo00sDb61CdYstpMI5aaCMnq9jySmdgTUmFnt/H/UH9goxQ4IFU24sqOGeMnq9ntLSUu1QZXtJIjnjkYnMacCqQ23YXV5yk6KYmZ0gBtvLoe0g6Iyju238ODwO1tStASJ4x6gqfS9qJmVx14Sb5x3eqgkdC+akYTCJFaDD721AVX2MK55MfLpYVXFW9+EbcqOLMmCZKB5rf38/dXV1gJxW+qQSSLRf2tsUHpz2ae1z+avgHhFCl+Zdil7Rc6DrALX94oSSbcF89MnJeHt7GXpPXD0Qk2hhXGE8AEe2iaJfvcFA8SJNv3Voo7jqACBqppZoD+9uC9PfBNpLFRUVjIyMhD1XIpGcOchE5jQgcFG4Zub48CpGoBpTdBFEJQqhdQ3rGHQPkmnLZFbaLCHmKC/HeeQIitFIXEhbyTXioXq3VlWZtEBMVFRVpWzDWkBrK4Vi36OV+K3TUlD04ulz8OBBAHJycoiPjz/eW5acoayYmolRr3CoeYCj7YNiMHshxGWBcwCOiCZ3ydZkFo/T1guEVmUUg4G4y7WqTCD5HkvxQu0cPbylJWyH0pTztCm96p1bGRkUt15HTU1BMerwdIzgahSPNT09ndTUVLxeL+Xl5R/krUskktMUmch8zLT0jbClWvPJCIgpg/i8cOB57XEE75iXK18G4KqCq9Ap4q+y/yUtFn3+cvQhSUXV7g48bh8J6VGkTRCnnNqqjtLT0oTBZA4u6AsezoiHkTLtWEOnlVRVZZ9frCmrMZ9cEm0mlk7Ufvdhol+dThT9hhBoL62sXonXJ2pa4q/Vqo1DGzaErSzIn5GK0aJnoMtBi38lRoDU3DxSc/Pxejwc3rRRPByLAesU/8qCXaLGRlGUYFVGtpckkjMbmch8zLy8txlVhfl5iWQlijb/VK2DwRZt03WRuD+pcbCR7W3bUVC4suBKIeZzuRjw71aKv+basO95eIvWVipemBFWASrbqFVjCuctxBwlHo99fweq24chLQpTVox4PI2NdHV1YTQamTx58gd895IzkUB76ZW9zeErC6bdqH2uWqtN241hadZSYk2xtNvb2dG2Q4iZCwq0lQVeL/2viRUbo1kfXGYaOHfHMuU8TSx8aMOasFiU363avr8Tn0tMnkpLS1EUhYaGBnp6esKeK5FIzgxO60TmZz/7GYqiCB8BD4hPAqqq8uLu0bZSGHue1D5P+zQYRNO5V6peAWB+xnwyo8V1BkPvvIO3vx9Dejq2hQuEWG/bMG01/Sg6hYnzxGklj8sV3HQdqa007B/Xts1JD0uA9uzZoz1v8mQsIUspJZ8szitOJSHKSNuAg41HQ8abkws1w0bVC/v+I4RMelPQ6TeS6DfOX5Xpe/GlME3LJH97qXpPB84Qk7vixUvRGwx01tXQXitOIZknxKFPtGgrC0I8ZWJjY8nLywNg715RLCyRSM4cTutEBrQLY2tra/Bj06ZNH/chnTR21PZQ0zVMlEkfvul6sB2O+PfTzLxVCHl9Xl6tehWAawpFATBA30t+ke+VV6KEGNJV+EW+OVOSsMWFOPLu3o5zeJiYpBSyJ4vtIVfzEO7mIdArYW0lh8MR3F0zc+bM93vbkjMcs0HPtf7E++ntjeFfMPOz2uc9Tx3TU2Zt/VoGXKKmJfbSS1GsVlw1NYzs3SfE0nJjSciw4XH7qAppE1mjYyiYoyXsh9aLVRlFp2Cb5a/K7Aof4Q6cr3v37pWeMhLJGcppn8gYDAbS09ODH8nJycf9eqfTycDAgPBxuvLfHQ0AXDk9k2iz6MfCvv9od7Xj50LqJCG0pWUL7fZ24sxxLMteJsTc7e0Mb9K8Y+KvvkqI+bw+KrZq5f5QkS/AwXfeBqDknGVh3jHDu7TnWScnoQ8xzzt06BBut5vk5GSysrLe721LPgHcOFf7Pa8/0kH7gDgWzeSrNE+Z3jqoe1cIlSaXUhBfgMPr4PVqcVmkPjqa2Iu0FmrfSy8KMUVRglWZ8s2R2kua6Ldi0wY8LnGnU9QsbYTbWdOPp1ucUJo4cSI2m42hoSGOHhUdgiUSyZnBaZ/IVFZWkpmZSV5eHp/5zGdoaGg47tc/8MADxMXFBT9O1wtr77CLN/3eMZ+emy0GfT7tbhZg1mfDnvtylSbkXZG3Isw7pv/V18DnwzprFqbcXCHWUN6DfcCFJdpIToh3TF97m+bFoSjBi0IA1e0NmuDZIuxkCrSVZs6cKb1jzhIKUmOYk5uA16fy/K6QqozJBqXXaY93PymEFEXhU0Xaksnnjz4f1kKK/5Sm6Rp88y18w8NCbOK8dHQ6hY66Abqbh4RYduk0YpJScAwPUbVrmxAzxJsxF8QDMLxbrMoYDAamT58OjJ7HEonkzOK0TmTmzZvHE088wapVq/jb3/5GbW0tS5YsYXBw8JjP+cEPfkB/f3/wo7ExQun7NODFPU24PD4mZ8ZSOi5ODNZvgt5aMMXAZNEDpsfRw/rG9QBcXSDGVJ+PvueeAyD+mvCWU/kmzcRs4tx09AbxV39gnea4mjttJvFpYrJiP9SN6vCiTzBjzo8XYq2trbS0tKDT6eRKgrOMQAL+zM5GfKGi30ACXvE6DIvbqy/PvxyL3kJVXxX7O8WJIeusWZhycvDZ7QysWi3EomJN5E7VKrIBH6QAOp2eyUs1XVdoewnANls7p+2721G94rEG2kuVlZX09fUd7y1LJJLTkNM6kbnkkku47rrrmDp1KhdddBFvvvkmfX19POe/WEfCbDYTGxsrfJxuqKrK0/620k3zssOrGIG72NJPaXe3Y3i9+nU8Pg8lSSVMTJwoxIY3bcLd1IQuNpbYSy8RYoM9DuoOaGLHyeeI4mCP2x384z/tfPF5AMP+7cO22ekoOvFYAyLJ4uJibDZb2HMln1wuLc0g1mKgqXeETVWikJaMaZAxHbyuMKffWFMsF+VqLaTnjz4vxBRFIe5arSrT97wYg1HR75GtbXhCtnBPWXo+KAr1B/bS1yYmOtaSJHRRBrz9LhxHxAmlpKQkcv3VSyn6lUjOPE7rRCaU+Ph4ioqKqKqq+rgP5X9iR20PNZ2ayPeKaWJSgb0HDvsnOkJEvj7Vx3NHtSTu2sLwsere/2rmefFXX4XOahVi5ZtaUFUYNzGehHQx4ajasYWRgX6iE5PImzlHiLm7RnDV9oPi1xqMjbndHDhwAIBZs0RDPsknH4tRH/Q+emZnhJZvoCqz50kIaSFdN1FrPa2uWx22SDL+6qvAYGBk3z4chw8LsewpSUQnmnEMu6naLU5MxaWmM2GaVl3Zt0Y05FOMOqL8bdGhrSHrFRg9f/fu3YsvRKAskUhOb86oRGZoaIjq6uozfhnh02NEvjEWUTjL/me0u9j0UsicIYS2tWyjfqCeaGN02KZrd3MzQxs2ABB/w41CzOvxUeZvK005J3zMe/9abTqqdNmF6EKmnIa3aXe2lqIEDPHilFNZWRkOh4O4uDgmTJjwfm9b8gnkRn976e2ydjoHQxZJTvkUGKOg6yg0iLqVqclTKUoowul18nqNKPo1pKQQe6Gm0+p9+mkhptMpTF6iJU8HN4Rv4Z52oeZiXbZ+DW6XeDzRc9M10W9lH+4uUfRbXFyM1WplYGDgjL9RkkjONk7rRObb3/42GzdupK6uji1btnD11Vej1+v59Kc//XEf2oemd9jFWwePI/Ld+Q/t8azbIKTl9N8KrUR/Rf4VRBlFs7re554HVSVq/nzMeWJSUbOvk5EBF1FxJiZMF6e+upsaaSo/hKLoKF0mmu75XF6G/SOrtgVi5UhVVbZv3w7A7Nmz0elO61NJcoqYlBHL9Kx4PD6V53eH6NEssTDFr9Xa9agQUhSF64q0qszzR8JFvwk33QRA/8rX8faLFZuSRZnoDJrot6NenEqcMGMWsSlpOIaHOLJF3NtkSLJiKdL2gwUS9ABGozGo8dq1a9cHeesSieQ04bS++jQ1NfHpT3+aiRMncv3115OUlMS2bdtISUn5uA/tQ/PC7iZcXh9TxsUydXy8GKxeBz01YI6DqWJVpXmomY1NmgX7DcXiugLV5aLvhRcASIiQ5B3aqN25lizORK+PLPLNmzWXmCQxybHv7UB1eNAnWYIXgABNTU20trai1+uld8xZzmfmaQn5v7fW4/GGtGXmfEH7XPZKmNPvZXmXYTVYqe6vZm+HqE2xzpqFuagI1eGg7+WXhVhUrIkC/0LIgxvFqoxOp2faBZrOa9/qN8KONZCQD+9uD3P6DbSXjh49Sm9v7/HeskQiOY04rROZZ555hpaWFpxOJ01NTTzzzDPk5+d/3If1ofF4fTyxpQ6Am+flhH/B9r9rn2d8BszRQui5I8+hojI/Yz55cXlCbGDNGrzd3RhSUohZdp4Q624ZoqWyD0WnMHmxWFVxu5zBlQSBP/4BVFVlaIvWjoqenxkm8t2xQ7OYLy0tlSLfs5zLp2WSaDPR0u9gTXmI6VzmDMiaDz437HpcCMWYYoJOv4FqYwBFUYJVmd7//hc1RLcy5VytRVq5sx3HkFuMnXcBeoOB9ppK2qpEbxhLUQL6BDPqiIeRA+JOp5SUlODfl8D5LZFITn9O60Tmk8baw+00942QEGXkqtAFkV1VULUGUEbvYv04vU5eqtTcem8sFis1AH0Bke9116EYRc1Nmf+OdcLUZKITxNUBh99bj3N4mNiUNHKninocZ00/nnY7ilGHbbYo8h0cHAw6+c6dO/cDvHPJJxmLUc9N/jbp4/5EXWDeF7XPux4Dj6hb+XSxVkFcU7+GtmGxYhN3+Qp00dG46xsY3rxFiKXnxZKcFY3X7QvbvxQVG8fEBUsA2Pe2WJVRdAq2eZrGbmhbuLHevHnzAM1Txul0hsUlEsnph0xkPkIe21wHaCPXFqMoqg1qYwovhCSx6rSqdhV9zj4ybBmcO/5cIeY4chT7rl2g1xN//XVCzOXwULFduzhMOVdMnFRVZfcb2pqDmZdcHu7k65/siJqZis4qug7v3r0bn89HVlYWmZkhU1eSs5Kb5+dg0CnsqO2hrEXUtDDpCojJgOEOrcU0huLEYuakz8GresOqMjqbjbirNa+k3v+GV2xK/VWZQ+82oYb42AREvxVb3mVkUNTR2Oakg0HB3TSEq1H0pCooKCAxMRGn0ym3YkskZwgykfmIONTcz47aHgw6hVvm54pB5yDs9S/Ym3dn2HOfqdAqLtdPvB6DTkwqep7SPGdizj8fY7poZHd4Sytuh5f4tCjGTxQ1LnX799DT3IjJamXKeRcKMU+fk5FyzcQsOkTk6/F4gmJIWY2RBEiPs3CJf1/YE/6EPYjeCHM+rz3e/rewUeybJ90MwAtHX8DutguxgOZraMMGXE2iHqZwbhomq4GBLgf1ZaLpXkbhRFIn5ON1uzmwTjTW09uMRJVqOruhzaEaG12wKrNjxw45ii2RnAHIROYj4nH/H/dLSzNIjwvZDr3vv+AahKRCyBM1Lvs69nGo+xBGnTFsQaSnq4uB11YCkHibuMrA51M58I42RTJteVaYxmX3G68AMOW8CzFHiRNQw9tawQfmvDiMIZ4zhw8fZmhoiOjoaEpKSj7Ym5ecFdy2MBeAV/e30D0U0paZ9TnQm6FlLzSJU0Hnjj+X8dHjGXANhI1im/MmYFu4EHw+ev/1LyFmNOmZtEhLnvatFSemFEVh5iXagsq9q1bi9Yg6muhFWoJuP9CFp0881mnTpmEymejq6qKmpuYDvnuJRPJxIROZj4DOQScr92utmtsXh/it+Hyw4xHt8bw7IaTF82SZVnFZkbeCREuiEOv97zOobjfWadOImiFqXGr3dzLQ5cBiMzJxvlip6Wqsp/7AXhRFx8xLLhcPx+VleIemHYheePyRa32I54zk7GZmdjxTx8fh8vh4ZmfIKLYtWXOqBtj+sBDS6/R8ZtJnAPj34X/jU8UqSOLnbgM0p19vyBLYacu0JL35SC+dDWKbqHjROUQnJDLc20PFZnF5pWl8DOa8OPCpDG0RqzIWi4UZ/n9PgfNdIpGcvshE5iPgP9vrcXl9zMyOZ3pWvBisXA3dldq24GmikLdhoIF1DesAuLUkxOXX4QjqBkKrMQD7/XeoU84dh9EkJhx73tS0MQVz5hOXGrJXaXc7PrsHfaIFyyRxsWRDQwNNTU3o9Xpmz579Ad655GxCURQ+tygXgKe21uHyhLRl5vpFv+WvQH+TELqq4CpsRhu1/bVsaRGFvbbFizEXFeGz2+l95lkhFpNooXC2Noq9d43oLqw3GJnhr8rsWvlSmFdNtN8ccnh7Gz6HRzxUf9u0srKSrq6Q9QsSieS0QiYypxiH28u/t2l/YD+3KIL77eY/ap9nfw7MMULoqfKnUFFZMm4JBQkFQqx/5Uq8PT0YMjOIuUDcVt1W209rdT86gxIm8rUP9FP+nrZ0cuZlVwox1acy+J52dxqzZByKXmxHbdmiXWCmTZtGdLQ4Hi6RgNY6TY0x0z7g5JV9Ic67mdMhdwn4PLD1r0Io2hQdXIL6r3KxhaQoCom3fw6Ann89hc/lEuLTL9Ampqp2dzDQLTr2Tj3/YowWq1aF3C9ut7YUJWBIjUJ1eoP7xAIkJSVRVFQEjJ73Eonk9EQmMqeYF3Y30TXkZFy8lYuniNUPGrZDw1bQm2Del4RQn6OPV6u0ysltk28TYqrPR88TWssp8ZZbUQyiADhQjSmak4YtTlwrsH/Nm3jdbtLyChk3UdS4jBzqwtvjQBdlCNur1NnZyZEjRwBYuHDhB3z3krMNs0HP5/3t04c3VuMN3Yq9+G7t8+4ntL1iY7hp0k3oFB1bWrZwpOeIEIu79FIMaWl4O7sYWLlSiKVkxTC+OAHVp3JgnVjpsdiimbpcE7PvfF001lN0CjH+dQdDm5tRQ8z8Fi9eDMC+ffsYCGlpSSSS0weZyJxCPF4ff3+3GoA7lkzAGOKqG6zGTL0BYsX9Uc8eeRaH18GkxEnMSRcXOQ6uW4eruhpdTAzx131KiPW126naoy3Tm36+uALB7XCw9y3tIjDrsiuFrduqqjL4rnYRsC3IRBfSjgrclRYXF5OcLDoASyRj+cz8HGItBmo6h1lTLlY6yF8O6VPBPTyqDfOTFZPFhTla0vGPg/8QYorJROKtWnu1+7HHwwzyZvirMmWbW3AMi8LemZdciaLT0XBwH+211UIsakYqumgj3n4XIwfEFlJ2djbZ2dn4fD62bt16Aj8BiUTyUSITmVPIGwdbaewZIdFm4oY5IXuVOo/CkTcABRZ+XQjZ3Xb+c1gbx75t8m1hCUf337ULQMLNn0Ef0uLZs7oeVMidmkzSODF28J3VjAwOEJeWHjQMC+Cs6cfdNAQGHdELxKRqYGAg6KmxaNGiE/shSM46os0GPuufYPrrhmpRm6IosPib2uPtD4NrWHjuF0o1M8i3696mtr9WiMXfcD266Ghc1dUMbdwoxLJKEkkaZ8Pj9FK+SdxuHZuSGjzfd4dWZQy6oKh98N2mMB3NkiXa83bt2oXdLo6GSySS0wOZyJwiVFXlbxu0u7/PLczFGlLhCFZjii+DlCIh9PzR5+l19mp3qLmix8vwli04Dh1CsVhIvOUWITbY4+DINu0OeNbF4goEj9vNzpWaO/DcKz4VtuV6cIPWjrLNTkMfbRJiW7duxefzkZ2dTVZW1gd5+5KznNsW5mIx6jjQ1M/mKtHjhZIrITEPRnphz1NCaGLiRJaOX4qKyqMHxUWT+uho4m+4HoDuR8OXUAa0MvvXNeIJ2aM0e4Wmv6nY8i597WKVyDYvA8Wow906jOOIuGOpoKCA9PR03G63XFsgkZymyETmFPFORQcVbYPYTHpuXZArBnvr4IBmcseiu4WQw+PgibInALij9I4wA7xANSb++uswJIrj2HvXNODzqYybmEB6XpwQK3/3HYZ6uolOSKTk3OVCzFk/gLOyD3QKMf5JjgBDQ0NBA7yAZkAieT+Sos3c6K9C/nVDlRjU6WHRN7THW/4MHlG8e8fUOwB4o+YNWobE6krirbeiGI2M7NrNcEhiUTg7jZhEC/YBF2Xvic9Lyysgd9pMVJ+PHa88J8T0NiM2fxVycF2DUJVRFCV43m/fvl2uLZBITkNkInMKUFWVP7+j/fG+eX4OcVHi/iPe+502uZG/DLJE/cvLVS/TNdJFhi2DFXkrhJh9z17sO3aA0UjS5z4nxgZcwZL6rEvEaozP62Xnq9p27NmXX4MhZB/T4DvaVFXUzFQMiaJZ39atW3G73WRmZlJYWPhBfwQSCXeck4dBp7Clupt9jX1icNqnITodBprhoJhYTE2ZyryMeXhUD48fEhdNGtPSiPvUtQB0/UWcfNIbdMFzf8/q+rCqzPxrNZfgso3rGOjsEGIxS8aDQYercRBnlXisJSUlJCYmMjIywp494uSTRCL5+JGJzClgw5FO9jX2YTHq+MIScVM1vfWw72nt8bnfF0Jur5vHDj0GwOenfB6jXkw4uv7v/wCIu+JyjBmijmXfmga8bh+pubFh6wi0cnor1phYpi6/WIi5Gge1croOYs8T20bDw8PBcvq5554raHUkkvdjXLyVK6drU0F/WlcpBg1mWPAV7fG7D4JXFOjeUapVZV6qfImuEVGEm3zHHWA0Yt++XdszNobiBRnHrMqMmziJ7CnT8Hm97Hj1eSGmjzERPU+bKhxYK1ZldDpdUBu2efNm3G7xWCUSyceLTGROMqqq8vs1RwH47IJcUmLE8Wc2/V6rxuQthex5QujlqpdpG24jxZrCVYVXCTH7rl0Mb9kCBgPJXxJHtYf7nRzcoE0czbksV0g4vB4PW1/QEqdZK67GaBErLgOBasz0VAxJViG2bds23G43GRkZQU8NieRE+OqyAvQ6hXcqOtjTIOpPmPN5sKVAby3sF5dCzk2fy9SUqbh8rvCqTGYm8ddo6zo6//IXIfZ+VZkF/qrMwXfWMNDVKcRizh0PBgVX/QDOGnHx5bRp04iLixNarRKJ5PRAJjInmbWHOzjY3E+USc8XzwmpxvQ1jC6HDKnGOL1O/n7g7wDcPuV2zPrRBEhVVTr/+CcA4q+5BtN4UceyZ1U9HrePtAmx5EwR3XjL332HvrZWouLimXmxuI7A1TSI43APKBATUo2x2+1Be3ZZjZF8WCYk27hmhlaVecif4Acx2UYnmDb+VtDKKIrCXVPvAjQrgvbhduGpyV/0V2W2bsO+e7cQO15VZnzJFLJKSvF5Pex4JaQqE2vWNmMDA2vqhaqMwWDgnHPOAWDTpk1SKyORnEbIROYkoqpq8I/1ZxfmkhQdUo3Z8GvwuWHCOZCzQAg9W/EsHfYO0m3pXD/xeiFm37YN+86dKEYjyV+6S4gN9jg45HfjnXdlnpBweNxutr6o3enOvfK6sGpM/+o6QKvGGFPExZGbNm3C5XKRnp7OxIkTT+CnIJGIfH15IUa9wnuVXWyvCZlgmn27ppXpb4C94gTT4nGLmZk6E6fXySMHRM8Z47hxxF+tTSJ1PvQHIel436rMdTcBmh1BX1urEItZmqVVZeoGcBwVK0jTp08nISFBaLlKJJKPH5nInETeOtRGeesANpOeL4ZqYzoqYL9fG7P8p0Jo2D3MPw/+E4AvTfsSJv3o+LOqqnT+6c8AxF9/fZg2Zvdbdfg8KpmF8WHamIPvrGawq5PohESmXXCJEHNU9WqTSnqF2AtEcXB/f3+wGrN8+XJZjZH8T2QlRnH9bK3i97u3j4peLUYrLLlHe/zug+AeXTGgKApfn6l5LL1U+RKNA+IiyuQvfwnFZNLaru+KSyHHVmUObhBXJWSVlJI7bSY+r5fNz/1biBnizEFfmYFVdahjnIn1ej1Lly4FNK2Mw+E4wZ+ERCI5FchE5iTh9vr4zaoKAD6/JI8Em+jFwrpfgOqD4hUwXly4+K/yf9Hr7CUnNocr8q8QYkPr1zOydy+K2UzSnV8UYn0ddg5v1u4o510hVmPcDgfbX9amQeZdcyMGk5gc9a+uByB6XkbYpNKGDRvwer3k5ORQUCDueJJIPgxfXVaAyaBjR10PG46K2hRmfRZix8Nga5jb76y0WSzKXIRH9fDX/eKUkjE9nYSbbwag4/cPCW6/eoOOOStyAdi9qi7M7XfxjZpLcMXmjXTU1QixmHOzUMx63K3DjBwUj7W0tJTk5GQcDofcwSSRnCbIROYk8d8dDdR120mONodrYxq2ay6+ig6W3yuEehw9PFmm7U366vSvCr4xqttNx28fBDT/DGNqqvDcbS9X4/OpZE9OIrMwXojtXPkSw709xKWmUbpMXCrpKOvG3TiIYtKFaWM6OzvZt28fAOeff76sxkhOChlxVj67QKv8/erNCnEHk8EMy36kPX73d2E7mL4282uA5isTuoMp+Yt3oIuJwXnkCANvvCHEJs7PIDHThtPuYc+qeiGWllcQdPvd9IzY0tLbjEE/pf6364UdTDqdjmXLlgHa2g65g0ki+fiRicxJYNDh5o9rtfHSu88vJNo8xsROVWHtz7TH0z8DKaLe5K/7/sqQe4hJiZPCXHz7XngBV20t+oQEkr54hxBrreqjem8nigILr80XYkO9Pexc+SIAS266Db1hdIxb9fqC2pjoxePQx4iVo3Xr1qGqKhMnTpQuvpKTylfPKyTOauRI+yAv7BbbREy9AdJKwdkPG38jhCYnTebCnAtRUXlw14OiHiY+nqQvaGsNOv/wR2Eztk6nsOBq7d/GgfVNYZuxF91wM4pOR+3eXTSWHRBi0YvHaTuYuh0MbxedgCdNmkRWVhYej4d33nnnw/0wJBLJSUMmMieBR96toXvYRV6KjRvmhFz8D78GDVvAYIGlPxBCVb1VPH9Um5z4zpzvoFNGfx3eoWE6/08bLU3+ylfQx8QEY6qqsvlFzXBv0uJMkjLFnUqbn/03HqeTjMKJFM0X3XiHtrbi6RxBN+auM0BtbS0VFRUoisLy5aL7r0TyvxIXZeRry7RW5e/ePsqw0zMa1Onhwl9oj3f+A7rF5Y53z7obo87IttZtvNf8nhBLvPUWDCkpuJub6f3Xv4RYzpQkxk2Mx+vxseM1cXdTQsY4pp6vacfWP/kPfL5RUbDOrCf2fK2CNLC2Hp99tDWlKAoXXqjddOzbt4+2tpDFmBKJ5CNFJjL/I029dv7xntZj/97FxeKGa/cIvP1j7fHCr0PcOOG5D+5+EJ/qY1nWsrAN192P/hNvdzemnBwSbhCnmKp2d9BeO4DBrGfuiglCrLO+lkMb1gBw7i1fED1lht0MrNV8Y2IvzEFnGa0c+Xw+Vq1aBcDs2bNJDWljSSQng1sW5JCdGEXHoJNH3hW1KeQvg4LzNZ+lQBXTT1ZMFjeXaHqY3+78LW7faGKhs1pJ+da3AOj669/wdI7qWhRFYeE1WvJ0ZHsb7XViK2jhdTdhttm0fzfr1wgx25x0DGlR+OweBtY1iMeTlcXkyZMBWLNGfJ5EIvlokYnM/8j9bxzG4fYxPy+RC0vSxODW/9O8Y2IyYfHdQmhT8yY2N2/GoDNwz+x7hJiroYGeRzWH35Rv34MyZqWA2+lly0taNWbmhdnY4kS/mQ1P/QNUlaL5ixk3cZLwugNr61EdHozptqBfRoA9e/bQ3t6OxWIJTmZIJCcbs0HP9y4uBuDv71bT1BuyUfqCX2hassOvQc0GIXRH6R0kWhKpG6jj+SOiB0zclVdgKS3FNzxMxx/+IMRSc2KZ6HftffeZo8IkUlRsHAuu1caxNz3zL5z20W3cil4hfoWmdxva2oq7QzzW5cuXo9PpqK6upqKi4sR+EBKJ5KQhE5n/gU2VXbx1qA29TuHnV0wRhbEDLfDe77XHF/xCM//y4/K6+PWOXwNwU/FNZMdmC6/b/ssHUF0ubAsXEHP++UJs91t1DPU4iUm0BLf9Bjiy9T0aDh3AYDSx5KbbhJi7fZjh7dqEU9zleSi60WMdGRkJ9vqXLl2KzWZDIjlVXFqazrwJiTjcPu57/bAYTJsMsz+vPX7zu4JJXowphq9M19Ya/GXfX+geGfWkUXQ60n6otW77X3qZkYOHhJddcE0+RouejroBDm8VvWOmX3QpCRnjGBnoZ9tLzwoxS2EClkmJ4FPpf6NG0OckJiayYIHmB7Vq1Sq5ukAi+ZiQicyHxOXx8dPXtD+Wty7IYWJ6jPgFb/8Y3HbImgelnxJCT5Q9Qd1AHUmWJO6cdqcQG1y/nqENG8BoJO3HPxaSo752O3v9raHF1xdiNOlHj2fEzsanNC+auVddR3zaaMVFVVV6X6kCH1hKkrDkxwvfc/369djtdpKTk5kzR2xxSSQnG0VR+MWVU9DrFFaVtbExdBx72Y8gKhm6jsCOvwuhawqvYVLiJAZcA/x+9++FWNSMGcRecTmoKu333SeMY9vizMy5TGvDbnulGucYzYveYGTprZpgeM+br9LVKE44xV2WB3oFx5FeHGWiod8555xDbGwsfX19bNq06cP9QCSSM5gRl5eazqGP9RhkIvMheXJLHdWdwyTZTNx9fsgeosq1cOhFrUR+ya9hTDLSONgYdCn99pxvE2uKDcZ8Tiftv3wAgKTP3oo5b3SMW1VV3n32KD6PNm49YVqy8C23vvgMQ709xKdlMOeKa4WYfXc7rtoBFKOO+MvF0fDm5uagS+kll1yCXq9HIjnVTEyP4baFuQD87LUynJ4x7rvWBDj/Z9rjDb+CgdEKikFn4Mfzf4yCwmvVr7GrTdx7lHrPPehsNkb276fvuZCt2ueNJyE9ipFBN9tXisLfCTNmkz97Hj6vl7X//IuQBBmTrUFhfN9r1fjGiJTNZjMXXXQRoLlh9/SIo+MSySedh9Ye5eI/vsd/tte//xefImQi8yE53KqJBr93STFx1jFbql12eEMTHjLvLsicEQypqsoD2x/A6XUyL30el024THjNrocfxt3YiCEtLWwxZPWeThrLe9AZFJbcUChUaroa6tjz5qsALPvcnYL5nXfYTf+b2h/t2PNzMCSMmt95vV5WrlwJwNSpU8nPF8e4JZJTyd3nF5ISY6a2a5iHN4QIf6d/BsbPAdcQrPqeEJqaMpVri7Rk/f7t9wvCX2NaGil33w1Ax+9+j7ujIxjTG3QsuUG76Ti4oYm22tHFkIqisOxzd2I0W2iuKOfQhrXC94xdloU+0YJ3wMXA2+If7JKSEvLy8vB6vbz55puic7FE8gnmYFM//3yvBpfHR3qs5f2fcIqQicyH5Pc3TOe5OxfwqZniCDMbfw199RA7Ds77oRB6u/5t3mt+D4POwA/n/1BIRhwVFXT/Q2sNpf3gB+jG6FQcQ27efUYzApt5UQ7xqaN7kXxeL6sf/iM+r5eCOfOZMEN0De5/sxaf3YMxPYroxZlCbPv27bS1tWGxWILjpBLJR0WMxchPVpQA8H/rKznaPjga1Ongst+DoofyV+HwSuG5d8+8mwRzAlV9VUFDyQAJN31aE/4ODtL+wANCLGtSoib8VWH9vyrwekYrL7HJqSz072F69z+PYx8Yk+gY9SRcpU0/DW1pwdU8WkpXFIVLL70UvV5PVVUVBw6InjQSyScRt9fHd188gE+Fy6dlsnxS2vs/6RQhE5n/gbkTEtGNEc3SdlCbVAK49EEwj+pmehw9/HL7LwFtu3Ve3Ji2kcdD649+DB4PMRdcQOzFFwnfZ9PzlYwMuknIsDH74lwhtvvNV2mrrsQcZWP57WIVx1HZi313OygQf3UhypjR8J6eHtavXw/ABRdcQHS06EUjkXwUXD41g+XFqbi9Kt994YDo+JsxdXTa7417YGR0iWOcOY5vz/k2oJlKVvVWBWOKXk/GL34Oej2Db61i0H+eB1h8XSHWGCM9LcPsWS1WV2ZeeiUpORNwDA3yzuOiPsdSlIB1ajKo0PvCUdQxSVBycnJw2u+tt95icHAQieSTzD/eq+Fw6wDxUUZ+ennJx3osMpE5WXic8NKdmgfGpMuh+FIh/MD2B+hx9FAQX8CdU0WBb88TT+AoK0MXG0v6vT8RYnUHuziyvQ1FgWW3FqM3jklGWprZ8qy29G7prV8gOjEpGPM5PPS+oG3its3PwJwzRovj8/HKK6/gdrvJyclhxozR9pdE8lGiKAr3X11KjNnAvsY+Ht8salc457uQXARD7bD6x0Lo8rzLOWf8Obh9bn6y+Sd4fKPaFcukSSR+9rMAtN57L57e0STIEm1k8fWFAOx6q46eltGRa51ez4V3fh1Fp+PIlnc5uk0U8MZfno8uyoC7dZiB9aI78cKFC0lPT8fhcPDmm29++B+KRHKaU905xB/8bvY/uayE5Gjz+zzj1CITmZPFhl9BRxlEJcFlDwmhNfVrWFW3Cr2i577F9wnbrZ2VlcHt1mnf/z6GlJRgzDHsZuPTWktp6vIs0ifEBWM+n5e3//5HPG4XOVNnMHmpOKbdt7IGb78LfZKFuEtE07zt27fT0NCA0WjkyiuvRKeTp4Hk4yM9zsIPL9M8jx58+wjVYycgjBa44s+AAvv+DUdXB0OKonDv/HuJMcZwqPsQT5Q9Ibxuyte/hik/H29nF+3/7z4hVjg7jZzSJHwelTWPlwktpvT8QuZddR0Aa//5V4b7RpMgfYyJ+Cu1FtPg+kahxaTX64P/ng4fPsyhQ+IIuETyScDt9fHNZ/fh8vhYUpjMNTPHvf+TTjHyCnYyaNwBm/+gPV7xB4geTUa6Rrq4b5v2R/T2KbczOWlyMOZzuWj+9nc0z5hzlhB39VXBmKqqbHz6CEO9TuJSrMwLmTba8coLNFeUY7RYueCOrwp6m5HD3cGWUuJ1RejGjGl3dnaybt06AC666CISExNP0g9BIvnw3Dgni8UFyTjcPu5+RvsjGSR7Psz3t01f/QoMjY5rp9nS+O7c7wJai2nsUkmdxULmrx4AvZ6BN99k4K23gjFFUTjvM8WYbQa6GofY8bpYCZp/7Y2k5ExgZHBAm2IaI+CNmpaCtTQZfCo9zx0RWkwZGRksXqytBXn99dfp7+9HIvkk8ed1lRxo6ifWYuA3n5p6WiwWlonM/4pzEF6+E1Sftviu5IpgyKf6+NGmH9Hj6KEooYi7pt0lPLXzoT/gPHIEfUICmfffL5wQR7a3UbW7A51O4YLPT8ZoHk1GWiuPsOX5/wCw/Pa7iEsdFVl5B1z0vqCV/KIXj8OcO1rF8Xg8vPTSS3g8HvLz85k1a9bJ/VlIJB8SRVF48LppxEcZOdjcz0Nrj4pfsPynkFoCw51aMjMmsbgy/0qWjl+K2+fmO+9+B7t71IHXWlpK8p1fBKDtZz/HPWYvki3ezHmf0VyG966up6WqLxjTG4xc/OVvotMbqNq5jYPvvC0cTvyV+ehsRjztdvrfEpOgc889l8zMTBwOBy+99BK+MaPcEsmZzO76Xv5vvaZH++U1pWTEWT/mI9KQicz/gqrCyruhp0abUrrk10L4qbKn2NKyBYvewm/P+a3QUhreupWexx8HIOP++4WWUn+nnXf/q/0hn3P5BNJyR/UtrhE7b/75QVSfj4kLllByzrLRw/Gp9DxbgW/YjTHdRtyFucLxrFmzhtbWVqxWK1dcccVpkUlLJAHS4yz86ppSAB7eWM3W6jHmc0YLXPtP0JuhcjXsejQYUhSFny/6OSnWFGr7a/nNTnF7dvJdd2GZPBlvfz/N93wb1TOqpcmfmUrxgnRtSf3j5TiGR0e5U3PzWHSDtt9p/ROP0NVQF4zpo00kXKeNcg9tbmGkfPRY9Xo91157LUajkfr6erZs2fK//3Akko+Z/hE333x2Hz4Vrp4xjhVTM9//SR8RMpH5X9jzFBx6QRsR/dTjmpGXn0Ndh/jjnj8C8P253ycvfrQ15G7voPk7Wjk8/sYbiFl2XjDmcXt5+59luJ1eMgrimHlRTjCmqipr/vEX+tpbiUlO4fw7viIkI4PrG3FW96MYdSTeVIwyRhh8+PBhtm/fDsDVV19NXNxopUYiOV24eEoGN8zOQlXh7mf30jnoHA2mTR41ylv1Q2jZFwwlWhL55ZJfoqDwYuWLrK4bo6UxmRj3+99pRnm7d9P55/8TvueS64uITbYw2O1g3ZOHhV1Mcy6/htxpM/G4nLz+x9/gdjqCMWtxItGLNX1A7wtH8fSNHmtSUhKXXqoJ/t955x0aGsSlkxLJmYSqqnzn+f009NgZF2/l51dODv2Cj+fA/MhE5sPSXgZvackIy38C2fOCoV5HL9/a8C08qocLcy7kmsJrgjHV7ab5m9/E29WFuaiItO+JZl/vPVtJR/0gZpuBC26fLIx371v9OhWbN6LodFz61Xuw2EZHpp01fQys1UZJ468swDjGa6a3t5dXX9UM8xYsWEBRUYgTsURyGnHv5SUUpEbTPuDka//dg8c7pjUz7y4ouhi8TnjuFrCPOunOz5jP50u1PU0/3fJTavpHTfZMOTlk3Pf/AOh+5BGG3hudRjJZDVz8xVL0Bh11B7rYu2Y06VB0Oi75yrewJSTS3dTAusceFvQycRfnYhwfjc/uoee/FYJeZvr06UyZMsj9548AADeISURBVAWfz8fzzz8vR7IlZyyPbqrl7fJ2THodf7t5JrGWMSawDdvg39fAcNfHdnwykfkwqCq8/k3wOKDgAlj4jWDI4/PwnY3foXW4leyYbH668KdC1aTjwd8xsmcPuuhoxv/pj+isoz3Gw1taKN/UAgpc+PnJxCSOOiU2HznMBv8upXNvvp3xk6aMfs8+B93/qQAVomakEjUrNRhzuVw8++yzOBwOxo0bx/Lly0/Jj0QiOVnYzAYevnkWNpOebTU9/PbtUQEvOh1c/TAk5Gqb5V++C8ZoUL48/cvMSpvFsHuYu9ffzZBrdKoo9pJLiL/xBlBVWr79bVyNo+PTKdkxwZHsba/W0Hx0dFIpKi6eS796D4qio2zDWva/PTparRh0JH26GMWsx1U/QN/ro8mToihcfvnlpKSkMDg4yAsvvIDXO2YVg0RyBrCzrocH3tK2u//k8hKmjo8fDfY1wrM3Q/U7mhnsx4RMZD4MigLX/AOKV2h/VMeML/9h9x/Y3rYdq8HKH8/7o7BLqX/lSnqe1FxIM3/9K0y5ucFYR/0AG/26mHmXTyC7ZNQTZqi3h9cfegCf10vRgiXMvPTKYMzn8tL9VLmmi8mwEX91QTBxUlWVV199lba2Nmw2G9dffz0Gg+GU/EgkkpNJQWo0v71uGgB/31jD6wdaRoPWBLj+X2CwaHqZjb8Khow6Iw+e+yCpUanU9tfyo00/wqeOJjppP/gBltJSvP39NH35y3iHRj1kJi/JpGheGqpPZdUjhxjoGgnGsqdMY/GnbwVg/ZOP0Fh+MBgzJFlJ/HQxKDC8rZWhHaO7ocxmM9dffz0mk4n6+nrWrFlz8n5IEskpprHHzl3/2o3Xp3LFtExunpc9GnTZ4ZmbNAF+Wulo2/djQCYyH5aEHLjxP2AbXd74StUrPFmuJSr3L76fgoSCYMy+Zw+tP/wRAEl33EHMmMrIYI+DN/5yAK/bR25pErPGuPe6HQ5e+c0vGOrtIXFcFhfd+TUhUel9sRJ3yzA6m4GkW0uEUetNmzZRVlaGTqfj+uuvl7oYyRnFpaUZ3LFE80C657n97G0YrZKQMVVbYQDaneCB0QWRydZk/rD0Dxh1Rt5pfIe/7PtLMKYzmxn/f3/GkJKCs7KKlu99L7ggUlEUlt5UTHJWNI4hN2/89QCukVFh8JwrrqV40bn4vF5W/v4B+jvagzFrcSKxF2p6tr5Xq3HWjY5dp6SkcNVVVwGwbds2du7ceXJ+QBLJKWTQ4eYLT+6ie9hFSUYsD1xTOtpdUFVterDtgLap/tNPg8l2/Bc8hchE5iSxtWUrP9/ycwDuKL2DC3IuCMZcDQ00feWrqG43MRecT8o37x6NjXh44y/7sQ+4SBpn44LbJ6P4dTE+n5c3/vwg7TVVWGNiufq792KyjmpfBt6uZ2R/J+gUkj4zSVgIefDgwaBfzKWXXkpOzqhoWCI5U/j+JZNYVpyK0+Pjjqd20dgzOlrNjM/AIn9b99WvQP3WYKg0pZSfzNdcsh858AgvV74cjBnT0hj/5z+hGI0MrVtHx4O/G42Z9Vz25alExZnoaRlm9T/L8HlHE50L7/waqbn5jAwO8NKvfsbI0KjuJWZpluYv41Xpfqocd+fosZaUlHDeeZqo/80336SqanSlgkRyuuH1qXzjmX0caR8kJcbMo7fNxmYeU81ffz+UvQQ6A9zwL4jPPvaLfQTIROYkcLT3aFDce8mES/jqjK8GY56eHhrvvAtvby+WyZPJ/PWvUfytKK/Hx+p/HKK7eZioWBOXfWUaJqt2sqiqysZ/PUb1rm3ojUau/M5PiE/PCL7u0LZWBv0W6QlXF2DOiw/GamtreeWVVwCYN28es2eLiyQlkjMFvU7hT5+ewaSMWLqGXHz+yZ302V2jX7D8Z9pKEK9LK3N3jvrPXF14NXeU3gHAz7f+nC3No2PQ1unTybhfM6rseewxup94IhiLTrBw6ZemojfqaCjrZuPTR4ICX6PZwpXf+THRiUn0NDfy6m//Hx6XdjyKopBwXRHGrBh8dg9djx3COzh6rOeccw7Tpk1DVVWee+452sZ42kgkpwuqqvKjlw/yTkUHZoOOf946W/SL2flPePe32uMVD0HOwo/nQMcgE5n/kcbBRr609ksMuYeYlTaL+xbdh07xJyqDgzR+4Q5ctbUY0tMZ/9e/oovSKio+n8qax8ppKO/BYNRx2VemCuLe7S89y543tUmji790N+MmTgrGRsq66HtVu6OLPT8b25z0YKytrY1nnnkGr9dLSUkJF10kLqCUSM40os0GHrttNmmxZo62D3Hb4zsZdvpbPjodXP0IZM6EkR7411WaCNjP12Z8jRV5K/CqXr654Zsc6BzdTB13xRWk3PMtADp+9Wv633gjGEvLjeWC20tQFCjf3Mq2V0ZFvLHJKVzzg59jjrLRXFHOm39+EJ9PE/HqTHqSP1uCIcmCt9dJ1+OH8Dm0Yw2If3NycnC5XPzrX/+iu3uMV45Echrwm9VHeGZnIzoF/njjdKZlxY8GD6+EN7RlrSz9Icy89WM5xlBkIvM/0Dbcxh1v30GHvYP8uHz+eN4fg6Z3PoeDpi99GUd5OfrERLIfexRjmjZNpKoqG/5TQfWeDnR6hUvuKiV1zFLHPW++yubnAssg76B40bnB2EhFD91PaxNKtjnpxCwfLel1dHTw1FNP4XQ6yc7O5uqrr5Z7lCSfCDLirPzr8/OIjzKyr7GPO57ahcPtnwAyRcFnXtCWSw40w1NXwVAHoCUPv1j4C+ZlzMPusXPX2rs43H04+LpJX/gCCbfcAkDL977PoL8dC5A/I5WlfuffPavr2fP26KbslOxcrvz2j9AbDFTu2MLqv/4hmMzoo00k3z4Fnc2Iu2WYrsfL8PkTL4PBwI033kh6ejrDw8M89dRT9PX1naofm0RyQjzybjV/21ANwC+vLuXiKaNdACrXwAu3AyrMug3O/e7HcoyRkFe5D0mnvZMvvP0FmoeayY7J5h8X/oM4syam9Y2M0PTlr2DftQtddDTZ//wH5jzNEE/1qbz3bCWHN7eiKHDB7ZPJnjw6obR/zVusf/IfACy87jPMumx0QslxtJfuf5eDV8Vamkz8VaMTSp2dnTz55JPY7XYyMjL49Kc/jdE4ZtZfIjnDKUqL4cnPzcVm0rOlupsv/Xv3aDJjS4JbXoG4bOiphqeuDCYzRr2RP533J2akzmDQNcgX13yRql6toqkoCmk/+D6xK1aAx0PT3d9k8J31we9ZsjiTBVfnA7D1pWr2vj1a7cmaPJXLvvFdFJ2O8vfW8/bDfw4Khw1JVpI/PwXFasBVP0DXE2X4XNqxWq1Wbr75ZpKSkujv7+epp56SO5kkHzuPvFvNL9/Uxqy/d3ExN84do3upXAvPfEZr4U66Ai79nTa9CzQONPLTLT/F5XVFetmPBJnIfAhUVeWejfdQP1BPpi2Tf174T1KitBUDvuFhGr94J8NbtqBERZH194exlJRoMZ9WiTm4oQkUOO+WYgrGeL7sfuNV1v5Tm7CYteJq5l97YzDmONJD11Pl4FGxTE4i8caJKHrtROro6ODJJ59keHiY9PR0brnlFqzW02MHhkRyMpmWFc8/PzsHi1HH+iOdfOHJXdhd/jZT3Di49RWIToeOcnj8UhjQxrajjFH8ZflfmJw0mT5nH7evvp3y7nJAM73L/NUDxF56CbjdNH/jG0IyM/OiHGZfmgvAlpeq2L2qLhgrnLuQy76uJTNlG9ey+uE/4fN7xZgyo0n5/BTNY6Z2QKvM+NtM0dHR3HrrrcTFxdHT08Pjjz9Ob++YqSyJ5CPkL+urgknM15cVcNe5Y5YUV67V9Gdep2Y58qnHQK9pOWv6a7ht1W28VPkSv9v1u0gv/ZEgE5kPgaIofH/u9ylKKOKfF/2TjGit/Obt76fhC3dg37nTX4n5J1H+xYxer491T5RT7q/ELP/sJCYt1HZVqKrKtpeeZcNTWiVmzpWf4tybbw9WW+z7O+h6shw8PiyTEjUDLr32q2tsbOTxxx9naGiI1NRUbrnlFqKiokIPWSL5xLAgP4kn/JWZTVVd3PbYTgYc/h1JSfnwuTchdjx0V2rJTK/WEooxxfD3C/5OSVIJvc5ePr/68+xp3wOAYjCQ+ZvfEHPxxahuN01f+xp9fsE8wLwr8ph7uTYKvu2VGra9Wh0UAE9csJhLAoZ5G9ey8qFfBQXApvExWmXGrMdV20/nPw7iHdJicXFxfO5znyMxMZG+vj4ee+wxOjtHN3tLJKcaVVV5cPURfrtaM5381gVFfOvCiaNj1gdfgP/eMCaJeRz0WqW/vLucz636HB0jHRTEF3DH1Ds+rreBoqof85KEU8zAwABxcXH09/cTGxv7/k84AXyqLyjsdTU103jnnbiqq9HFxpL96D+xlmoL8FwjHlb94xCN5T3BbdaBSozP6+WdJx5h/9ua0HDh9Z9h/jU3Bk+koa0t9L1WDSpYp6WQeF0RikH7nlVVVTz77LO43W7Gjx/PTTfdJJMYyVnD7vpebnt8B4MOD8XpMTx22xwy4/2VyN56ePJy6KsHWyrc9CyMmwnAkGuIr77zVXa378ait/Cbc37DednaaLTqdtP645/Q71/pkfqd75D0+dtHv+equqDwt3hhBks/MxG9/6aicvsW3vjTb/B6PGRNnsqV3/4R5ijNW8PVPETXY4fwDbsxpFhJvn1K0C5hcHCQp556is7OTqxWKzfeeKO0S5CcctxeH99/8SAv7mkC4LsXT+TLS0e9z9j2MKzyr9CZ8im46m9g0DSg7za9y7c3fpsRzwglSSX8/fy/E2+JP+nH+EGv3zKROQmMHCqj8a678HZ1YUhLI+uRv2OZOBGAoV4Hr//fAbqbhzCYdFx0xxRySzUTPZdjhDf++Btq9uwERWHpLV8IamJUr0r/GzUMbdFK47b5GcRfkR/0mNmxYwdvvfUWqqqSn5/PDTfcgMlkinB0Esknl0PN/XzuiZ10DjpJjTHz2G1zmDLOb/w40AL/uQ7aD4ExSrubnHgxAA6Pg3s23sO7Te+ioHDP7Hu4teRWFEVB9fnoePB39Dz2GKAtdk3/0Y9Q/Jqzsvea/SPZkD05kQu/MAWz3zah4dABXn3w/+EaGSFpfDZXffde4tO0qUJ3p52uRw/h7XOiizaSdEsJZr/If3h4mKeffprm5mb0ej1XX301U6ZMQSI5FfSPuPnq03t4r7ILvU7hl1dP4YY5fk2M1wOrfwg7/q7997y74KIHgg72zx15jl9u/yVe1cv8jPk8tPQhok3Rx/hO/xsykfFzqhOZvldeoe2nP0N1OjFPnEjW3x/GmK794Wqp6mP1I4ewD7j8PjFTg9NJPS3NrHzoAboa6jAYTVz6tW9TOE+bx/eNeOh++jDOyj4AYi/IIWZZFoqi4PV6eeutt9i1axcAU6dO5YorrpCrByRnLc19I3zu8R0cbR/CatTz609N5YppWtsWxwA8/1ltFwwKLPsRLL4HdDo8Pg8PbH+A545qrsDXFl7LD+b9ALPeDED3Y4/T8dvfgqoSNXcu4/74BwwJ2ob7ugNdrP7HITxuH/FpUVxyVymJGVr1pb2mKujGbYmJ5Ypv/YCsEq066+l30v1kGe6WYdArJFxTiG1WGqDtRXvppZeoqNC0CkuWLOG8886Tk4eSk8rR9kG++NQu6rrtWI16/vqZmZxX7Ndq2nvghc9BzQbtv5ffC4u/BYqC2+vmVzt+Ffz3cmX+lfx04U8x6k7dUIlMZPycqkRGdblo/9Wv6X36aQBs5yxh3O9/jz46GlVVOfBOE1terMLnU0nMtHHZV6YSm6SVvY9u28Tqh/+Ia2SEqLh4rvrOT8go1Co4rqZBup+uwNvjQDHqSLxhItYpWgWnt7eXF198kaYmrRR4/vnns2jRImEppURyNjL2DhPg9kUT+MGlxRj1OvC64c1vw+4ntC+eeBlc/TewxKGqKv8+/G9+u/O3qKhMSpzE75b+jqyYLAAG31lPy7e/jc9ux5iZSeaDDxI1cwag7Ud76+GDDPU6MZr1LL9tEvkztAvCUE83r/z2PtprKlF0OhZdfzNzr/wUik6Hz+Wl99kjjJRpHjK2eenEr8hDMerx+XysWbOGrVs1l+K8vDyuvfZabLaPz/5d8snhjQOtfOeF/dhdXsbFW/n7LbNGK5jNu7Xx6t46MNrgmkdg0goAOuwdfGvDt9jfuR8Fha9M/wpfnPpFFEXBNzJC34svkfCZm076tUgmMn5ORSKjqiqNd3yR4U2bAEj+8pdJ/sqXUfR67AMuNvyngtr92h/UwjlpnHdzMUazHo/bzab/PsHuN7T++7jiyaz4xneJTkxCVVWGtrTQ/2YteFX0CWaSbi7BNE4r2ZWVlfHaa6/hdDoxm81cc801TPS3ryQSiWar/ru3j/BXvw/GjOx4Hrp+OrnJ/iRg95NaQuN1QXyO9oc6ez4Am5s38/33vk+fs48YYww/X/Tz4JoRZ2UljV/5Ku6GBtDrSfnqV0j64heD/95X/+MQLf7q6eQlmSy6rhCjSY/b6WDNI//H4U0bAG3x5CVfvYfohERUn8rAugYG32kAFYwZNhJvKsaYomncDhw4wMqVK3G73cTGxnLVVVeRlzdmkkQiOQEGHW5+vrKcF3ZrN8EL85P4v5tmkmgzadvjt/0F1v4MfB5t3cCN/4V0rbW5rmEdP9vyM+3fhimGXy35FeeMPwcAx+HDNH/7O7iqq0n74Q9IvPXkGuTJRMbPqarIDKxaTeu995L5618R49+hUrOvkw3/qWBk0I1Or7DwmgKmLhuPoii011Sx6q8P0dWoTVDMueJaFt94Kzq9Hk+fg94XK4OtJOvkJBI+VYTOasDhcPD222+zZ482XTF+/HiuvfZaEvwlbolEIrK6rI1vP7+fQYeHKJOen6wo4cY5WmuW5t3w/G2a+6+igyX3wDnfBYOJtuE2vr3x2+zv3A/AZXmX8YO5PyDOHId3aIi2n/+CgZUrAYiaM4fMXz2Acdw4vF4f21+pYe8azWMmIT2K5beVkJYbi6qqlG1Yy7rHH8bjdGKNjeOCL36VwjkLAHBU9tLzzBF8w24w6Ii7MIfoxeNQdArt7e08++yz9PT0ADB//nyWL18u/aEkJ8Suuh6++dw+GntGUBT48tJ8vnl+EQa9Tqu+vPZ1qN2ofXHJlXD5n8Aaz5BriAd3PciLlS8CaNXKc39HVmwWqstF198foevvfwePB0NqKpm/+Q22+fNO6rHLRMbPqdTIePv60MfHYx9wseXFKo5s13anJI2zcf7nSkgeH4Pb6WD7y8+x49UXUH0+rLFxXHjn1ymYPQ/VpzK8o43+t2pRnV4Uo464SyZgW6CNcx8+fJg333yToaEhQOuZL126FL1ef8xjkkgkmm7mnuf2sa3GnwTkJXLfVaUUpEaDox/e+h7s/6/2xSmT4PI/QvY83D43f9v3Nx499Cg+1UeqNZXvzf0eF+RcgKIo9L/6Km0//wU+ux3FaiXlq18l8dZbUIxGGg/3sPaJcuz9LlCgdOl45l+Rh8lqoLu5kTf+8Gs6G+oAKJizgGW330lMYjLeASc9zx8N3siYsmJI+FQhxjQbLpeL1atXs3v3bgASExO57LLLyM/P/6h/pJIzjEGHm9+vOcqTW+rwqTAu3spDN0xn7oRE8Hlh56NaFcY9DAYrXHQfzP48KArvNLzD/dvvp8PegYLC56Z8jq9O/ypGvRFHeTktP/wRTr+WK+bCC0n/2U8xJCae9PcgExk/p3T82uvj4IZmdrxei2vEAwrMvDCbuSvy0BkUKrdvZsO/HmWwS/OGKJq/mOWf/xJRsXE46/rpW1mDu1lLUkw5sdrCuWQrvb29vPXWWxw9qi3AS0xM5PLLL2fChAkn9fglkk8yPp/Ko5tq+d2aIzjcPox6hTvPyeerywqwGPVQ9rK2N8beBSgw67Nw3o8gOpUDnQf40aYfUTdQB8D8jPn8YN4PyIvLw1VfT+uPf4J9504AzBMnkv6znxI1YwYjQy42PV/J0e3tANjizSy5oZC86Sl43W62vfQMO197EZ/Xi8lqZdENtzLtgkvQ6fXYd7XT93oNqtMLeoXoReOIXZaFzmLgyJEjrFy5MnhTM3nyZC666KJTNokpOXNRVZU3Drby/14vp33ACcA1M8fxsysmE2sxQsN2rcXa5t87lrMIrvgzJOXTMNDA73b9jnca3wFgfPR4frHoF8xJn4Onp4fOP/+ZvmefA58PfUIC6ff+hJiLtUnAU6HVlImMn1OVyDQf6eXdZ4/S0zIMQEp2DOfcWER6XhztNVW8+5/HaDiknSgxySksvfULFM1bhKfXwcDb9dj3+nfBWPTEXpBD9IJM7CN23nvvPXbu3InX60Wn07F48WKWLFkiy8kSyYekscfOT18r450K7d9cZpyFb5xfyLUzx2Nw9sHbP4F92m4zTNGw6G5Y8BUcOh2PHXqMRw8+isvnwqAzcFPxTdw+5XYSLYn0v/wKHb/5DV7/rqSYCy8k5e67MedNoLG8hw3/PcJA5wgAGflxLLg6n4yCeDob6ljzyJ9prdRMyBIyMll8460UzluEd8BF38tVOCq0SpLOZiT2ghxsc9Jxup2sX7+eHTt2oKoqJpOJBQsWsGDBAiwWCxLJ1upufrWqgv2NfQDkJEXx/66cwjlFKdBdDet/CYde0L7YHAfLfwKzP0+fa4C/H/g7zxx5Bo/Pg17Rc9vk27hz2p1YfHp6nn6arr/8Fd/gIAAxl1xM+o9/jD4xkeo9nexZXc9lX5mKLc58Ut+PTGT8nKpEZuWf99NQ1o3ZZmDBVflMWpRJZ30NW194mupd2wHQG43MueJTzL3yWhQ7DK5vZHhXO/hUUMA2O53Yi3Jw6b3s2LGDzZs34/I7gubl5XHxxReTmpp6vMOQSCQfAFVVWV3Wzi9WltHS7wAgL8XGty4o4pIpGegbtsDbP4KWvdoTYjJg8Tdhxi00Orr59c5fs7FJ0xFYDVZuKr6J2ybfRvSI+v/bu/foqMrz0ePfvffcMpMwuTAJJCQQEhSUOyQIgoBQpctL+Xm0dtlqaqmtivZ4+LX1slbhx2otra3KEa+rtmHV450WadVaFAUVBQSJIkiEECQXQq6TZGaSue33/DFh0lRBVMJM0uez1l6T2e/ek2feTPY8+9mXl8bf/5729S/ETpo0DNKvuIKsH/8YPXsYu175lIpXjxAJx8ZgGjVxKKWXFpI1wsmeTf9k63NP0tURG2dpWPFZnH/VdymYOIXgJ17aXzpEpCcRsgxNIW1ePs4pHo4ea+Cll16irq4uFk9KCrNnz6akpETuJfUfaveRNv7vpgNsroxV/502gx9dMJob5xbh8NXCm/dAxdOgooAGU74HC1bgtVh5ev/TPLHvCTrDsSRldt5slk1bRpGzAO+6dbQ8/kciR48CYB83jpw778A5vYTqD5vZ+fJhmo7E1pu0IJ/ZV405re9LEpke/ZXIeI8F+PD1GkouK6Tp8MfsenkDh3btAEDTdMaefwGzvv09nGYavq11BN5vjCUwgL04HfeiUQRSo2zbto1du3YRDsdusT5s2DC+8Y1vyDFwIfpBdzjK/9v2KQ9vrqLVH9tpGJXlZMnsQq6cmkfKJxtg08rYycAAziyYcROU/pCtrftYs3sNe1v2xposTq4YcwXXjLuG7IZuGu9fje/1WEkew2DIxReTueQHmHnF7Hipmo+3HkX1bANGjM1gykUFZI90sOulF9j14nrCwViC5RlZSMnl/4uzSs+na2cTHa99ihmIjdFkpNtJmzuClKnZVFZ9wuuvv05zc+wKyZSUFEpKSigpKSEtLe1MdalIENNUvFHZyGNvHmJHdayCZ9E1rplRwK0XjsHTuQ/efRj2/jV2NRLAWYtg/l00DMnhz/v+zLpP1tEViSXLZ2eczX9P/29KU8/F+5e/0vqnPxHpGTLD4vHg+d8/IfWyb3FgVxO7Nx6hrSEAgNVuMGlhPpMXFsRvDHm6SCLTo78SmXB3N/veeoPdr/ydltqejZ6mMXbWBcxYfDXO9hR879QTqu6Ir2MvTidtQT610Wbef/999u/fj9kzWm52djZz5szh3HPPlRtgCdHPOrvD/PHtav70djUdPQM5pjutfKekgKumeCg68ld4Z01siAOI3Rn43CtQU8vYjI+HKh6msi12aEhDY17+PL4z9jtMrLfS+vAj+N95N/67nCUlpF91JZFJs9n12lEO7mqMJzRZeS7OmZ1L3tl2Pnx1A3s2/TOe0KQN9TBh/kWcM2s++oEInW/VYvpiOzya3cA5NRtnaQ77Gg6yZcsWvD2HuAzDYPz48UydOpWCggK5z9Qg0+ILsn53HU/vOEJVU+zUBquhcfmkPH5ywQhGNm+GHX+AI72fQUbPIzrvLt41wjxf+TxbarcQVbHBTcdmjmXJhCXM8Y2g49lnaf/7i6ju2GfQMmwYWTf8EG3eJezf0cL+d48S6OgZRyzFwvi5eZw9I52aj3Yw9vy52E/zEDmSyPTor0Tm+V/eFT8Hxmp3cM4FC5g4eSHWWo2uD5vie1Do4Dg3i+5zHRxo+5SKigra29vjr1NYWMisWbMoLi6WDY4QZ5g/GOH5nTX8cWs1Na1d8fnTRmbw7SnDuMTYTurOB2PDHByXfS7mxG/zrqeAJ2o2srVua2+TM5tLR1/KpZFzca57jY6XX4ae0bD1tDSGXHoJxrxLqGxIY9/Wo0RCsR0Zw6IzeoqHUeNdNB95l4qNL8YPOaFpjJo0lXNmzWe4GkXXe01EW7rjv9NWkIZjYhZHUtrYXvEeNTU18bbMzEwmT57MhAkT5JYNA1hXKMqWT5r42wd1vLrvGOFo7Gs71W7hu6Uj+NHoVrIOroOP1kOw53OjW1DnXsGB8Zfxiv8wL1e/TJ2vLv6a03Omc0POf3HWrkY6Xn6Z4L6P4232MWNwfedaWvLP48D7zdTu7x2Z3em2MWHeMJypRzmw/U2qd79HNBJh0c3/h3PnLjit71sSmR79lcjs3bKJ7X95jpLSyxmeUkT4QAdRbzDerqVa8I2z8qmjhcpDn9DS0hJvczgcTJw4kalTpzKsZzgDIUTiRE3Fq/uO8dzOGjZXNh4/CoyuQemoDL6X18B838u4Dv4dIr1JBHnTOFR0AU/TycvHttER6q3AFroL+aazhNkVIRyvvEOkvj7eZvF4sC9YRGP+bA7W22mp88fbrA6DgnFDcDhrOFb1LrUf74m3GRYLIydOZVzR+WR0ZBE+2AlmT6MG9tFuWnMj7PNV83FVZfycO4gdth43bhxjx44lOztbdpySXEd3mDf2N/LKRw1srmyiKxyNt03Lc7K08Cizo9uxHXgFfA3xtog7n4/GfoO3M3LY2PAO1e3V8bY0ayrX2i7gwrp07Ns/oqvn/mQAWK1YFlyGf/ol1LQ5qf24DfP4P4IGecV23J4mOpv38+mH7xPqCsRXzR5ZxIz/uoqzZs4+vX0giUxMfyUybS9VEdhxLHapJKBQdNqCNOeFqLd6OdJcRyDQ+4c2DIOioiLGjx/PuHHj5CokIZJUY0c3f91dx4aKej4+2tGnbbIHrne/z/nBt8lq3oGmzHhbKDWHNwsm8Teb4q2OKiIqEm/LsLq53FvIzA+CZO6sAn/vtgGHg/C0hTTkzaLOn0HA3/uauq6ROTyMRiXeox/Q2dL7hQWQm382Y/NmkhUeht7ad1NuZlio8XRQ2VVDTXMd/7qpd7lcFBYWMnr0aAoLC6VakwS6QlF2ftrKO1UtvFPVwp5abzyhNoiyYEgd3/EcppS9pB7bCT3ntpjAYecQ3h85nXdcqWzrrKIzFDsBF6UY3mnhMv8YZhx1kV5RTfRYY/x3hi1OuqddRHvx+TSGMvA29e6MKxXC5W4ldUgLQf9hjlV/Aj2fIR2d4Z4xjCuezVDbCGgIkXXtOTjGnN7PkSQyPforkWn428dUb6uk2eGj2RmgMdRKV6i7zzI2m40xY8Ywbtw4iouL5RJJIQaYmtYAG/cdY+PeBt473Br/YgEYSjvXuiv4huUDxnRVYDV7//87NY2tmcN5PX0ob+HHZ4bjbZaIYmadiwurnRR/3IHd25vUKDQ6PWfRNmY+x5xj8EV6txlKKTStFZv9MJHuKgLeOuBfkhNrOmcNLyXXVYQrmIameisu3YSoSfPyqa2F2q5jRMzevXuA1NRU8vLyyMvLIzc3l+HDh8v4Tv0oFDE50NjJntp2PqhtZ0+dl8qGTsJRhY7JaK2e8dphZrtqmOGoIbf7AEbYjwIaDIMDNit70zL5ID2HPaqLjkjsM5QWUIw6pji7xc701nQKDvuxtMSS8YjhwO8ahi9jNP5R02h3jqCzK3ZyrlIRVLQFZTbicLajzKP4W2tjnzk0XJZ0suzDyfOMJduVjz3ogGhsB95PkDbdT8H5Z5N3ybjT2k+DKpF56KGH+N3vfkdDQwOTJk1izZo1lJaWntK6/ZXIPP3nJ6k8dKDPPMMwyMvLi+/p5OXlyajUQgwS3kCIbYda2XaohW2HWtjf0BlvsxFmul7JBfoe5tn2Mcasxug55hMG9tptvOdwsMOVRoXNQrfWs9lVirwWmFitmHLEYNynUezB3opMlyOLtvSz8GaNoy1zLEGjN7lQZhdm5Ahm5AiYNUTD3nibRbOS7ShgmHM0OamjSNMz0IglNlFMjuntHNVbqbd6aVTtKD77NeB0Ohk6dChDhw7F4/EwdOhQ3G43brcbu/303i9kMIpETRo7gxxt7+JQk5+qJj8HG30cavLxaaufVNNHvtbIaK2B0Xo9hVoDZ1mOUUg9QS1IvcWg3mKh1mKh2mblgN1Blc1GOBwlxws5bYocLwxrU+R6dQqbdJw+Rbcjg25HJt2OLPzOHPyuXALufLr0VFABVLQN0/SizDZU1IuueYmEW7BrDpyWIaRa0xlizWKINYv0lBxchpswJj6tG5/WTYcWoE334zX8eLUA4Z7K4+LFi5k8efJp7cNBk8g8++yzXHfddTz66KPMmDGD1atX8/zzz1NZWXlK91jpr0Tm7bffZvfu3fG9mBEjRpCTkyOJixD/IVr9ISpq2thT28Geunb21rdztOceNQ6CTNCqmaIfYKp+kLHaEQq0RnRNEQI+ttvYZ7Ox125jn91GldWKqWlopiK3FYrrFcX1ijFHFSOawBaN1V66Ujx0phXQkVZAZ1oBnakFRC2xqo0y/ZiRBsxoAypyFDPaBKrnPjSajSz7cLIceWTahuG2eUi1pgMQJkqL1kmT3kGz3kGT1kGH3vU577iXw2ZnSNoQ3EPcuNPdONNcOJ1OnE4nKSkpfR7tdvugOR8nHDVpC4TwBsK0+UO0BUK0BcKxR1833rYWAt4mgh1NqEArQ1QnGZoPj9ZGpt7MEKMNm9GOYenEZyhaDYNWXccXNfBFDDoiBr6ogaVbI8MH6X5Fut+CO+AgtduJK5SGI5pGyJpKyOoiaEslaLXTbXPSbUshbLWC2Y1SPpTpw1BBbFoEm6awaxp2w45dd2IzHNgMF4bViW5NAYuVkBalWwvTrYXoJoxfC8aTl4gWPWGf6LpOVlYWc+bMYeLEiae1vwdNIjNjxgxKSkp48MEHATBNk/z8fG699VbuuOOOL1y/vxKZWJl3cPxzCiFOjxZfkIONPqqa/FQ1+TjY6KOmLUC9twvCXRRrdZyl1XKWXsdorZ4RWjMjtEZsejef9ux5H7ZaqLZaqbZaqbMYdGo6OV7Ib1aMaIbcFkW2V5HdDumdGiF7BgFnTs80DL8zh64UD0G7G6W6MaPNscMG0RaU2dEztWPRDNy2obhtHtzWobgsblzWdFwWN+gG7Vpsj9ur+/Fqftq1LvxaNyEt8oX98K80BVYMDHQMtPika1rvPC02aYCmxW53r2v01JA0NE3F5seecnzTq/fUkhSgHf85Xu0yUcrEVApME6UUSpmonp9N0wQz2vPcREVij7F5CpRCqWisTcVeT1OK4zfH0OI1rp6flQZKQykdTRmxRwxAB3Q0paNremxpZaArDU3TUWigGyhdB01H6Vrsu0XTY6uioaHi7xm95x1rQM86xyelaaBrKE1DAREtSpgoYS1KiAhhLUqYCBGiqC/59eVyuXC73WRkZODxePB4PGRnZ5OZmdlv4/8NikQmFArhdDpZt24dixcvjs8vKyvD6/WyYcOGz6wTDAYJBntPWOro6CA/P79fxloSQohToZSiLRCm3tsVnxo6gngDIVr9IYK+Vuy+WlzdR0kJtjCUdjyal6FaO6m6F2XpJGAN0mGJ0GLRaDUM2nSdDmVg+nWsnTqWLo20gEa6X+H2Q1pAJy2Yhj2aga5lELVmELSl9+zNOwlZrAQtJmE9QlQLo1QAzABKBbARwWlYSdFtOAw7DsMVnyxGChGrQchQdOsmAT1EkDDdWrj3UQvTTZioZn5x54iE0TUdpyMFl8uFK82F0xWrrLlcLtLS0uKHEt1ud0IuUDnVRCapj4M0NzcTjUbJycnpMz8nJ4f9PSNv/rtVq1axcuXKMxGeEEKcEk3TyHTZyHTZGJ/nPumyUVPh647gC0XwByP4grFHglGM7jBDun24Au0UBDvQg+0YwQ6IBCDSRTTiIxT1EzYDeKN+mukiqoKEtSYi0Vq0cAQtFMUIRbGEohghEz1iYonoaFE7etSOZtrRTRtR004g4qArakNhBwxUrDbQZ9I1A4tuxWJYSdNsZOgWDF3HwAG6hmloKEOPVQqOVwuM2GNUU5gamJrC1ExMQGn0VFZiJ5OaPY9KAxOFqcUqL2ZPLeZ4Feb4HrmKV2lU7/Pjr9FTu/nqvni///OWiFVseqs4Wm+tKf48VnHqma/FqjI6GlpPhUbTNDRdx6obGBYLFosVq82K1WbDYrVgWC1YrVYMmwWrzYrFbsXmdGBPsWO327HZbNjtfX+2Wq2D4shCUicyX8Wdd97JsmXL4s+PV2SEEGIgMHQNt9OK2ym3aBDiVCR1IjN06FAMw+DYsWN95h87duyEN5I7nnEKIYQQYvBL6kF9bDYb06ZNY9OmTfF5pmmyadMmZs6cmcDIhBBCCJEMkroiA7Bs2TLKysqYPn06paWlrF69Gr/fz/XXX5/o0IQQQgiRYEmfyFx99dU0NTWxfPlyGhoamDx5Mq+88spnTgAWQgghxH+epL78+nTor/vICCGEEKL/nOr3d1KfIyOEEEIIcTKSyAghhBBiwJJERgghhBADliQyQgghhBiwJJERQgghxIAliYwQQgghBixJZIQQQggxYEkiI4QQQogBSxIZIYQQQgxYST9Ewdd1/MbFHR0dCY5ECCGEEKfq+Pf2Fw1AMOgTmc7OTgDy8/MTHIkQQgghvqzOzk7cbvcJ2wf9WEumaVJfX09aWhqapiUkho6ODvLz86mpqZHxnv6N9M3JSf+cnPTPyUn/nJj0zcklQ/8opejs7CQ3NxddP/GZMIO+IqPrOiNGjEh0GAAMGTJE/mFOQPrm5KR/Tk765+Skf05M+ubkEt0/J6vEHCcn+wohhBBiwJJERgghhBADliQyZ4DdbmfFihXY7fZEh5J0pG9OTvrn5KR/Tk7658Skb05uIPXPoD/ZVwghhBCDl1RkhBBCCDFgSSIjhBBCiAFLEhkhhBBCDFiSyAghhBBiwJJE5gy7/PLLKSgowOFwMHz4cK699lrq6+sTHVZSOHz4MEuWLKGwsJCUlBSKiopYsWIFoVAo0aElhbvvvptZs2bhdDpJT09PdDgJ99BDDzFq1CgcDgczZsxgx44diQ4pabz55ptcdtll5ObmomkaL7zwQqJDShqrVq2ipKSEtLQ0srOzWbx4MZWVlYkOK2k88sgjTJw4MX4jvJkzZ/KPf/wj0WGdlCQyZ9j8+fN57rnnqKys5C9/+QtVVVVceeWViQ4rKezfvx/TNHnsscfYu3cv999/P48++ih33XVXokNLCqFQiKuuuoqbbrop0aEk3LPPPsuyZctYsWIF77//PpMmTeLiiy+msbEx0aElBb/fz6RJk3jooYcSHUrS2bJlC0uXLmXbtm28+uqrhMNhLrroIvx+f6JDSwojRozgN7/5Dbt27WLnzp1ceOGFfOtb32Lv3r2JDu3ElEioDRs2KE3TVCgUSnQoSemee+5RhYWFiQ4jqZSXlyu3253oMBKqtLRULV26NP48Go2q3NxctWrVqgRGlZwAtX79+kSHkbQaGxsVoLZs2ZLoUJJWRkaGevzxxxMdxglJRSaBWltbefLJJ5k1axZWqzXR4SSl9vZ2MjMzEx2GSCKhUIhdu3axcOHC+Dxd11m4cCHvvvtuAiMTA1F7ezuAbGc+RzQa5ZlnnsHv9zNz5sxEh3NCksgkwO23347L5SIrK4sjR46wYcOGRIeUlA4ePMiaNWv48Y9/nOhQRBJpbm4mGo2Sk5PTZ35OTg4NDQ0JikoMRKZpctttt3H++eczfvz4RIeTNPbs2UNqaip2u50bb7yR9evXc8455yQ6rBOSROY0uOOOO9A07aTT/v3748v/7Gc/Y/fu3WzcuBHDMLjuuutQg/gGy1+2fwDq6upYtGgRV111FTfccEOCIu9/X6VvhBCnx9KlS/noo4945plnEh1KUjn77LOpqKhg+/bt3HTTTZSVlbFv375Eh3VCMkTBadDU1ERLS8tJlxk9ejQ2m+0z82tra8nPz+edd95J6tLd1/Fl+6e+vp558+Zx3nnnsXbtWnR98ObbX+Wzs3btWm677Ta8Xm8/R5ecQqEQTqeTdevWsXjx4vj8srIyvF6vVDj/jaZprF+/vk9fCbjlllvYsGEDb775JoWFhYkOJ6ktXLiQoqIiHnvssUSH8rksiQ5gMPB4PHg8nq+0rmmaAASDwdMZUlL5Mv1TV1fH/PnzmTZtGuXl5YM6iYGv99n5T2Wz2Zg2bRqbNm2KfzmbpsmmTZu45ZZbEhucSHpKKW699VbWr1/P5s2bJYk5BaZpJvV3lCQyZ9D27dt57733mD17NhkZGVRVVfGLX/yCoqKiQVuN+TLq6uqYN28eI0eO5Pe//z1NTU3xtmHDhiUwsuRw5MgRWltbOXLkCNFolIqKCgCKi4tJTU1NbHBn2LJlyygrK2P69OmUlpayevVq/H4/119/faJDSwo+n4+DBw/Gn1dXV1NRUUFmZiYFBQUJjCzxli5dylNPPcWGDRtIS0uLn1fldrtJSUlJcHSJd+edd/LNb36TgoICOjs7eeqpp9i8eTP//Oc/Ex3aiSX2oqn/LB9++KGaP3++yszMVHa7XY0aNUrdeOONqra2NtGhJYXy8nIFfO4klCorK/vcvnnjjTcSHVpCrFmzRhUUFCibzaZKS0vVtm3bEh1S0njjjTc+97NSVlaW6NAS7kTbmPLy8kSHlhR+8IMfqJEjRyqbzaY8Ho9asGCB2rhxY6LDOik5R0YIIYQQA9bgPgFBCCGEEIOaJDJCCCGEGLAkkRFCCCHEgCWJjBBCCCEGLElkhBBCCDFgSSIjhBBCiAFLEhkhhBBCDFiSyAghhBBiwJJERgghhBADliQyQoh+8/3vfx9N0z4z/es4QF/V2rVrSU9P//pBCiEGNBk0UgjRrxYtWkR5eXmfeck24nc4HMZqtSY6DCHEVyAVGSFEv7Lb7QwbNqzPZBgGGzZsYOrUqTgcDkaPHs3KlSuJRCLx9e677z4mTJiAy+UiPz+fm2++GZ/PB8DmzZu5/vrraW9vj1d5/ud//gcATdN44YUX+sSQnp7O2rVrATh8+DCapvHss88yd+5cHA4HTz75JACPP/4448aNw+FwMHbsWB5++OF+7x8hxNcjFRkhxBn31ltvcd111/HAAw8wZ84cqqqq+NGPfgTAihUrANB1nQceeIDCwkIOHTrEzTffzM9//nMefvhhZs2axerVq1m+fDmVlZUApKamfqkY7rjjDu69916mTJkST2aWL1/Ogw8+yJQpU9i9ezc33HADLpeLsrKy09sBQojTJ9HDbwshBq+ysjJlGIZyuVzx6corr1QLFixQv/71r/ss+8QTT6jhw4ef8LWef/55lZWVFX9eXl6u3G73Z5YD1Pr16/vMc7vdqry8XCmlVHV1tQLU6tWr+yxTVFSknnrqqT7zfvnLX6qZM2eewjsVQiSKVGSEEP1q/vz5PPLII/HnLpeLiRMnsnXrVu6+++74/Gg0Snd3N4FAAKfTyWuvvcaqVavYv38/HR0dRCKRPu1f1/Tp0+M/+/1+qqqqWLJkCTfccEN8fiQSwe12f+3fJYToP5LICCH6lcvlori4uM88n8/HypUrueKKKz6zvMPh4PDhw1x66aXcdNNN3H333WRmZvL222+zZMkSQqHQSRMZTdNQSvWZFw6HPzeuf40H4A9/+AMzZszos5xhGF/8JoUQCSOJjBDijJs6dSqVlZWfSXCO27VrF6Zpcu+996LrsWsSnnvuuT7L2Gw2otHoZ9b1eDwcPXo0/vzAgQMEAoGTxpOTk0Nubi6HDh3iu9/97pd9O0KIBJJERghxxi1fvpxLL72UgoICrrzySnRd54MPPuCjjz7iV7/6FcXFxYTDYdasWcNll13G1q1befTRR/u8xqhRo/D5fGzatIlJkybhdDpxOp1ceOGFPPjgg8ycOZNoNMrtt99+SpdWr1y5kp/85Ce43W4WLVpEMBhk586dtLW1sWzZsv7qCiHE1ySXXwshzriLL76YF198kY0bN1JSUsJ5553H/fffz8iRIwGYNGkS9913H7/97W8ZP348Tz75JKtWrerzGrNmzeLGG2/k6quvxuPxcM899wBw7733kp+fz5w5c7jmmmv46U9/ekrn1Pzwhz/k8ccfp7y8nAkTJjB37lzWrl1LYWHh6e8AIcRpo6l/P5gshBBCCDFASEVGCCGEEAOWJDJCCCGEGLAkkRFCCCHEgCWJjBBCCCEGLElkhBBCCDFgSSIjhBBCiAFLEhkhhBBCDFiSyAghhBBiwJJERgghhBADliQyQgghhBiwJJERQgghxID1/wGn+Q62X55wBQAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Let's find the linear part or the Jacobian matrix around the fixed points. We decompose Jacobian matrix and then visualize its stability."
   ],
   "id": "d76d23dc21601cff"
  },
  {
   "cell_type": "code",
   "id": "73fa353f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:41:35.310688Z",
     "start_time": "2025-10-06T03:41:32.088994Z"
    }
   },
   "source": [
    "import jax\n",
    "\n",
    "# select the first ten fixed points\n",
    "fps = jax.tree.map(lambda a: a[:10], finder._fixed_points)\n",
    "\n",
    "# compute jacobian and visualize the decomposed jacobian matrix\n",
    "J = finder.compute_jacobians(fps, plot=True, num_col=2)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\codes\\projects\\BrainPy\\brainpy\\version2\\dynsys.py:325: UserWarning: \n",
      "From brainpy>=2.4.3, update() function no longer needs to receive a global shared argument.\n",
      "\n",
      "Instead of using:\n",
      "\n",
      "  def update(self, tdi, *args, **kwagrs):\n",
      "     t = tdi['t']\n",
      "     ...\n",
      "\n",
      "Please use:\n",
      "\n",
      "  def update(self, *args, **kwagrs):\n",
      "     t = bp.share['t']\n",
      "     ...\n",
      "\n",
      "  warnings.warn(_update_deprecate_msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x1000 with 10 Axes>"
      ],
      "image/png": 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dd2jbtm29yxcXF8Pf3x8WiwV+fn7VXq8t2GpjAFDbL+9O7/zhc72r7biteYWY9MmRasvWtYwUSm1HaXodl57Uto9+6eSbj6KZd91/D9b3nm0I5tVtUvMKUPY9p9f3t17HpUdS95UreSWpGVNL37598eCDD2L58uUAAJvNhtDQUEyZMgVJSUn1Ll/XL8rZYGsIA4BgfzP2zHzE4eOBh975l8NfRvUtI4VS21GaXselJ/XtozuO/2kwfM1edc4jRzOmFj3kFaDse06v72+9jkuPXNlXruSVpBP41VBRUYHDhw8jNjbWPs1oNCI2Nhb79++vcZny8nIUFxc7/ChJACi0lOFQ/jX7tEP51+r8n1RNy0ih1HaUptdx6Ul9++iOb374WYFq1KGXvAKUfc/p9f2t13HpkVr7qsHnjCUmJmLhwoVo3rw5EhMT65w3JSXF5cJ+7erVq7BarQgKCnKYHhQUhNOnT9e4THJyMhYsWCB7Lc76saSsxscNXcbVbTbmdpSm13HpSUN/9/+50Xj7iHkl3a/3n5LvOb2+v/U6Lj1Sa181uBnLzc1FZWWl/XFtDCp9xUlNZs2a5RDExcXFCA0NVbyOtr7mGh83dBlXt9mY21GaXselJw393Qf5+jRaDcwr6X69/5R8z+n1/a3XcemRWvuqwc3Yrl27anyslDZt2sDDwwNFRUUO04uKihAcHFzjMiaTCSaT8ldv3XHns+Wo//1uKwCICm+Fdv5mXLGU1XgybU3LSKHUdpSm13HpSUP30UNd2zRaDcwr59X23lHyPafX97dex6VHau0rzZwz5u3tjT59+iA7O9s+zWazITs7W5Yv/f1h8XCnlzHU8viXz+ePuNfhJD8Po8F+eXlDl5FCqe0oTa/j0hPuI/3kFaDs/tTrvx29jkuP1NpXkq6mHDVqVI2H9w0GA8xmMyIiIvDss8+iW7dushR5x7p165CQkIAVK1YgKioK7733HrKysnD69Olq52bUpCFXOjhzlRLvM6YevY5LT+TYR3JcTcm8uo33GVOfXselR1L2leK3thg/fjw2bdqEgIAA9OnTBwBw5MgRXL9+HYMHD8axY8fwww8/IDs7G7/97W+dXX2dli9fbr+JYs+ePfHBBx+gb9++DVq2ob8o3oFfG/Q6Lj359T6KbO+PR9/bDQDYmRgDH2+POpeXoxljXvEO/O5Er+PSoxtlVej/7i7YbAIp8T3xcNe678CveDOWlJSE4uJiLF++HEbj7U86bTYbXn/9dfj6+mLRokV4+eWXceLECezZs8fZ1TcaLd+ziEgPSiuqcO+8bQCUu+kr84qIpFAyrySdM7Z69WpMnTrVHmzA7XvoTJkyBStXroTBYMCrr76KvLw8KasnIpIN84qI3J2kZqyqqqrGe+WcPn0aVqsVAGA2m93qsnEiapqYV0Tk7iR9UfjYsWPx/PPPY/bs2XjwwQcBAF9//TXefvttjBs3DgDw1Vdf4b777pOvUiIiCZhXROTuJDVjqampCAoKwpIlS+z30QkKCsK0adMwc+ZMAMDgwYMxZMgQ+SolIpKAeUVE7k5SM+bh4YE5c+Zgzpw59u9P+/XJah07dnS9OiIiFzGviMjdSWrGfolX+RBRQxlgQNe2LeyPlca8IqKGUjKvJDdjGzZsQFZWFgoKClBRUeHw2pEjR1wujIj0x8fbAzsSYxTfLvOKiJylZF5Juprygw8+wIQJExAUFITc3FxERUWhdevWOH/+PIYOHSp3jUREkjGviMjdSWrG/vKXv2DlypVYtmwZvL298cYbb2DHjh147bXXYLFY5K6RiEgy5hURuTtJzVhBQQH69esHAPDx8UFJSQmA25eQf/755/JVR0S6cqvCikEpX2FQyle4VWFVZJvMKyKSQsm8ktSMBQcH49q1awBuX4V04MABAEB+fj4kfLsSETURAgJnfryBMz/egIAyWcG8IiIplMwrSc3YI488gr///e8AgAkTJmDatGkYNGgQ4uPjMWrUKFkLJCJyBfOKiNydpKspV65cCZvNBgB45ZVX0Lp1a+zbtw+PP/44Jk6cKGuBRESuYF4RkbuT1IwZjUaHL90dPXo0Ro8eLVtRRERyYV4RkbuTfJ+xsrIyfPvtt/jxxx/tf3Xe8fjjj7tcGBGRXJhXROTOJDVjW7duxbhx43D16tVqrxkMBlitylwlRURUH+YVEbk7SSfwT5kyBXFxcSgsLITNZnP4YbARUW0MMKB9gA/aB/go9nVIzCsikkLJvDIICdd2+/n5ITc3F126dGmMmhpNcXEx/P39YbFY+B11RBogx3uWeUVESnDlPSvpyNjTTz+NnJwcKYsSESmKeUVE7k7SkbHS0lLExcUhMDAQkZGR8PLycnj9tddek61AOfEvTSJtkeM9y7wiIiW48p6VdAL/559/ju3bt8NsNiMnJwcGw/99lmowGNw23IhIXWWVVjyzYj8AIGtiNMxeHo2+TeYVEUmhZF5JasbmzJmDBQsWICkpyeH+PUREdbEJgW8vWeyPlcC8IiIplMwrSclUUVGB+Ph4BhsRuT3mFRG5O0nplJCQgHXr1sldCxGR7JhXROTuJH1MabVasWTJEmzbtg3du3evdkJsSkqKLMUREbmKeUVE7k5SM3b8+HH06tULAJCXlydrQUREcmJeEZG7k9SM7dq1S+46iIgaBfOKiNydU83Yk08+We88BoMBX3zxheSCiEjfWjX3VmQ7zCsicpVSeeVUM+bv799YdRBRE9DM2xNH5g5SZFvMKyJyhZJ55VQzlp6e3lh1EBHJinlFRFrBG+8QERERqYjNGBEppqzSivgV+xG/Yj/KKq1ql0NEVCsl80rS1ZRERFLYhMDB/Gv2x0RE7krJvOKRMSIiIiIVsRkjIiIiUhGbMSIiIiIVsRkjIiIiUhGbMSIiIiIV8WpKIlKUj5eH2iUQETWIUnnFZoyIFNPM2xOnFg5RuwwionopmVf8mJKIiIhIRWzGiIiIiFTEZoyIFFNWacWE9EOYkH6IX4dERG5NybziOWNEpBibENj13X/sj4mI3JWSecUjY0REREQqYjNGREREpCLNNGOLFi1Cv3790KxZMwQEBKhdDhFRrZhXROQMzTRjFRUViIuLw6RJk9QuhYioTswrInKGZk7gX7BgAQAgIyND3UKIiOrBvCIiZ2imGZOivLwc5eXl9ufFxcUqVkNEVDvmFVHTpetmLDk52f4XKhGpr5m3J35YPFztMtwS84rIvSiZV6qeM5aUlASDwVDnz+nTpyWvf9asWbBYLPafixcvylg9ETUlzCsiaiyqHhmbPn06xo8fX+c8nTt3lrx+k8kEk8kkeXkiojuYV0TUWFRtxgIDAxEYGKhmCUREDcK8IqLGoplzxgoKCnDt2jUUFBTAarXi6NGjAICIiAi0aNFC3eKIiH6BeUVEztBMMzZv3jxkZmban/fq1QsAsGvXLvTv379B6xD/+91SvEqJSBvuvFeFxr7HknlF1PS4klcGobWUc8GlS5cQGhqqdhlE5KSLFy+iQ4cOapehKOYVkTZJyasm1YzZbDZcvnwZvr6+MBgMdc5bXFyM0NBQXLx4EX5+fgpV2Lj0OCaA49IaZ8YlhEBJSQlCQkJgNGrmC0NkwbzS35gAjktrlMorzXxMKQej0eh0t+rn56erf1iAPscEcFxa09Bx+fv7K1CN+2Fe3abHMQEcl9Y0dl41rT81iYiIiNwMmzEiIiIiFbEZq4XJZML8+fN1dRNGPY4J4Li0Rq/jUpMef6d6HBPAcWmNUuNqUifwExEREbkbHhkjIiIiUhGbMSIiIiIVsRkjIiIiUhGbMSIiIiIVNZlmLC0tDZ06dYLZbEbfvn1x6NChOudfv3497r77bpjNZkRGRmLLli0OrwshMG/ePLRr1w4+Pj6IjY3FmTNnGnMINXJmXKtWrcLDDz+Mli1bomXLloiNja02//jx42EwGBx+hgwZ0tjDqMaZcWVkZFSr2Ww2O8yjxf3Vv3//auMyGAwYPny4fR6199fu3bsxYsQIhISEwGAwYNOmTfUuk5OTg969e8NkMiEiIgIZGRnV5nH2/ao3zCvmldb2F/PKxbwSTcDatWuFt7e3WLNmjThx4oR48cUXRUBAgCgqKqpx/r179woPDw+xZMkScfLkSfHHP/5ReHl5iePHj9vnWbx4sfD39xebNm0Sx44dE48//rgIDw8Xt27dUmpYTo/r2WefFWlpaSI3N1ecOnVKjB8/Xvj7+4tLly7Z50lISBBDhgwRhYWF9p9r164pNSQhhPPjSk9PF35+fg41X7lyxWEeLe6vn376yWFMeXl5wsPDQ6Snp9vnUXt/bdmyRcyZM0f87W9/EwDExo0b65z//PnzolmzZiIxMVGcPHlSLFu2THh4eIitW7fa53H296Q3zKvbmFfa2l/MK9fyqkk0Y1FRUeKVV16xP7darSIkJEQkJyfXOP8zzzwjhg8f7jCtb9++YuLEiUIIIWw2mwgODhZLly61v379+nVhMpnE559/3ggjqJmz4/q1qqoq4evrKzIzM+3TEhISxBNPPCF3qU5xdlzp6enC39+/1vXpZX+lpqYKX19fcePGDfs0d9hfdzQk3N544w1x3333OUyLj48Xjz76qP25q78nrWNe1Yx5pa39xbxyLq90/zFlRUUFDh8+jNjYWPs0o9GI2NhY7N+/v8Zl9u/f7zA/ADz66KP2+fPz83HlyhWHefz9/dG3b99a1yk3KeP6tdLSUlRWVqJVq1YO03NyctC2bVt069YNkyZNwk8//SRr7XWROq4bN24gLCwMoaGheOKJJ3DixAn7a3rZX6tXr8bo0aPRvHlzh+lq7i9n1ffekuP3pGXMq9oxr7S1v5hXzu0r3TdjV69ehdVqRVBQkMP0oKAgXLlypcZlrly5Uuf8d/7rzDrlJmVcvzZz5kyEhIQ4/EMaMmQI/vrXvyI7OxvvvPMOvvrqKwwdOhRWq1XW+msjZVzdunXDmjVr8OWXX+KTTz6BzWZDv379cOnSJQD62F+HDh1CXl4eXnjhBYfpau8vZ9X23iouLsatW7dk+XetZcyr2jGvtLO/mFfO7ytPl6slTVq8eDHWrl2LnJwch5NHR48ebX8cGRmJ7t27o0uXLsjJycHAgQPVKLVe0dHRiI6Otj/v168f7rnnHqxYsQILFy5UsTL5rF69GpGRkYiKinKYrsX9ReQs5pW2MK+cp/sjY23atIGHhweKioocphcVFSE4OLjGZYKDg+uc/85/nVmn3KSM6453330Xixcvxvbt29G9e/c65+3cuTPatGmDs2fPulxzQ7gyrju8vLzQq1cve81a3183b97E2rVr8fzzz9e7HaX3l7Nqe2/5+fnBx8dHlv2vZcyr6phXzq/TVcyr25TMK903Y97e3ujTpw+ys7Pt02w2G7Kzsx3+Ovml6Ohoh/kBYMeOHfb5w8PDERwc7DBPcXExDh48WOs65SZlXACwZMkSLFy4EFu3bsUDDzxQ73YuXbqEn376Ce3atZOl7vpIHdcvWa1WHD9+3F6zlvcXcPu2BeXl5Xjuuefq3Y7S+8tZ9b235Nj/Wsa8csS8uk0r+wtgXknOK6dO99eotWvXCpPJJDIyMsTJkyfFSy+9JAICAuyXE48dO1YkJSXZ59+7d6/w9PQU7777rjh16pSYP39+jZeKBwQEiC+//FJ8++234oknnlDl0mNnxrV48WLh7e0tNmzY4HBpcUlJiRBCiJKSEjFjxgyxf/9+kZ+fL3bu3Cl69+4tunbtKsrKytx2XAsWLBDbtm0T586dE4cPHxajR48WZrNZnDhxwmHsWttfdzz00EMiPj6+2nR32F8lJSUiNzdX5ObmCgAiJSVF5ObmigsXLgghhEhKShJjx461z3/nUvE//OEP4tSpUyItLa3GS8Xr+j3pHfPq/2pmXmlnf93BvJKWV02iGRNCiGXLlomOHTsKb29vERUVJQ4cOGB/LSYmRiQkJDjMn5WVJe666y7h7e0t7rvvPrF582aH1202m5g7d64ICgoSJpNJDBw4UHz33XdKDMWBM+MKCwsTAKr9zJ8/XwghRGlpqRg8eLAIDAwUXl5eIiwsTLz44ouq/E/QmXFNnTrVPm9QUJAYNmyYOHLkiMP6tLi/hBDi9OnTAoDYvn17tXW5w/7atWtXjf+m7owjISFBxMTEVFumZ8+ewtvbW3Tu3NnhPkR31PV7agqYV8wrre0vIZhXruSVQQghnDuWRkRERERy0f05Y0RERETujM0YERERkYrYjBERERGpiM0YERERkYrYjBERERGpiM0YERERkYrYjBERERGpiM0YERERkYrYjFGTNH78eIwcOVLtMoiI6sW80j82Y+R2xo8fD4PBAIPBAC8vL4SHh+ONN95AWVmZ2qURETlgXpEcPNUugKgmQ4YMQXp6OiorK3H48GEkJCTAYDDgnXfeUbs0IiIHzCtyFY+MkVsymUwIDg5GaGgoRo4cidjYWOzYsQMAYLPZkJycjPDwcPj4+KBHjx7YsGGDfVmr1Yrnn3/e/nq3bt3w/vvvqzUUItI55hW5ikfGyO3l5eVh3759CAsLAwAkJyfjk08+wUcffYSuXbti9+7deO655xAYGIiYmBjYbDZ06NAB69evR+vWrbFv3z689NJLaNeuHZ555hmVR0NEesa8IinYjJFb+sc//oEWLVqgqqoK5eXlMBqNWL58OcrLy/H2229j586diI6OBgB07twZe/bswYoVKxATEwMvLy8sWLDAvq7w8HDs378fWVlZDDcikh3zilzFZozc0oABA/Dhhx/i5s2bSE1NhaenJ5566imcOHECpaWlGDRokMP8FRUV6NWrl/15Wloa1qxZg4KCAty6dQsVFRXo2bOnwqMgoqaAeUWuYjNGbql58+aIiIgAAKxZswY9evTA6tWrcf/99wMANm/ejPbt2zssYzKZAABr167FjBkz8Oc//xnR0dHw9fXF0qVLcfDgQWUHQURNAvOKXMVmjNye0WjE7NmzkZiYiO+//x4mkwkFBQWIiYmpcf69e/eiX79+mDx5sn3auXPnlCqXiJow5hVJwaspSRPi4uLg4eGBFStWYMaMGZg2bRoyMzNx7tw5HDlyBMuWLUNmZiYAoGvXrvjmm2+wbds2fP/995g7dy6+/vprlUdARE0F84qcxSNjpAmenp549dVXsWTJEuTn5yMwMBDJyck4f/48AgIC0Lt3b8yePRsAMHHiROTm5iI+Ph4GgwFjxozB5MmT8c9//lPlURBRU8C8ImcZhBBC7SKIiIiImip+TElERESkIjZjRERERCpiM0ZERESkIjZjRERERCpiM0ZERESkIjZjRERERCpiM0ZERESkIjZjRERERCpiM0ZERESkIjZjRERERCpiM0aa0b9/f/Tv31/tMoiI6sW8ImewGaNGk5GRAYPBYP8xm82466678Oqrr6KoqEjRWj777DO89957DZq3tLQUaWlpGDx4MNq1awdfX1/06tULH374IaxWa+MWSkSq0GpeAcDbb7+N3/zmNwgMDITZbEbXrl0xdepU/Oc//2m8IklW/KJwajQZGRmYMGEC3nzzTYSHh6OsrAx79uzBf//3fyMsLAx5eXlo1qxZg9dXUVEBAPD29na6lsceewx5eXn44Ycf6p03Ly8P3bt3x8CBAzF48GD4+flh27Zt2LhxI8aNG4fMzEynt09E7k2reQUATz31FAIDA3H33XfD19cXp06dwqpVq9C2bVscPXoUzZs3d7oGUpan2gWQ/g0dOhQPPPAAAOCFF15A69atkZKSgi+//BJjxoxp8HqkhJoUwcHBOH78OO677z77tIkTJ+L3v/890tPTMXfuXERERChSCxEpS2t5BQBffPFFtWnR0dF4+umn8T//8z8YPXq0YrWQNPyYkhT3yCOPAADy8/MBAFVVVVi4cCG6dOkCk8mETp06Yfbs2SgvL3dY7tfnYOTk5MBgMCArKwuLFi1Chw4dYDabMXDgQJw9e9Zhuc2bN+PChQv2jyA6depUa31t2rRxaMTuGDVqFADg1KlTUodORBrj7nlVmzvLXL9+3ellSXk8MkaKO3fuHACgdevWAG7/9ZmZmYmnn34a06dPx8GDB5GcnIxTp05h48aN9a5v8eLFMBqNmDFjBiwWC5YsWYLf/e53OHjwIABgzpw5sFgsuHTpElJTUwEALVq0cLruK1euALjdrBFR06CVvBJC4KeffkJVVRXOnDmDpKQkeHh48CICrRBEjSQ9PV0AEDt37hT/+c9/xMWLF8XatWtF69athY+Pj7h06ZI4evSoACBeeOEFh2VnzJghAIh//etf9mkxMTEiJibG/nzXrl0CgLjnnntEeXm5ffr7778vAIjjx4/bpw0fPlyEhYVJHkt5ebm49957RXh4uKisrJS8HiJyT1rPq8LCQgHA/tOhQwexbt06534JpBp+TEmNLjY2FoGBgQgNDcXo0aPRokULbNy4Ee3bt8eWLVsAAImJiQ7LTJ8+HQCwefPmetc/YcIEh/MzHn74YQDA+fPn5RoCXn31VZw8eRLLly+HpycPKBPplVbzqlWrVtixYwf+53/+B2+++SbatGmDGzduuLROUg7/r0KNLi0tDXfddRc8PT0RFBSEbt26wWi8/XfAhQsXYDQaq50QHxwcjICAAFy4cKHe9Xfs2NHhecuWLQEAP//8syz1L126FKtWrcLChQsxbNgwWdZJRO5Jq3nl7e2N2NhYALevxhw4cCB++9vfom3btnjsscdcWjc1PjZj1OiioqLsVyfVxmAwSF6/h4dHjdOFDHdtycjIwMyZM/Hyyy/jj3/8o8vrIyL3puW8+qV+/fqhXbt2+PTTT9mMaQA/piRVhYWFwWaz4cyZMw7Ti4qKcP36dYSFhcmyHSnh+eWXX+KFF17Ak08+ibS0NFnqICLtcue8qklZWRksFoss66LGxWaMVHXnY79f3206JSUFADB8+HBZttO8eXOnQmn37t0YPXo0/uu//guffvqp/WMKImq63DGvbt68idLS0mrTv/jiC/z888/1HuUj98CPKUlVPXr0QEJCAlauXInr168jJiYGhw4dQmZmJkaOHIkBAwbIsp0+ffpg3bp1SExMxIMPPogWLVpgxIgRNc574cIFPP744zAYDHj66aexfv16h9e7d++O7t27y1IXEWmHO+bVmTNnEBsbi/j4eNx9990wGo345ptv8Mknn6BTp054/fXXZamJGhebMVLdxx9/jM6dOyMjIwMbN25EcHAwZs2ahfnz58u2jcmTJ+Po0aNIT09HamoqwsLCag23/Px8+1+lr7zySrXX58+fz2aMqIlyt7zq0KEDnnrqKfzrX/9CZmYmKisrERYWhldffRVz5syx3x+N3Bu/m5KIiIhIRTwRhoiIiEhFbMaIiIiIVMRmjIiIiEhFbMaIiIiIVMRmjIiIiEhFbMaIiIiIVNSk7jNms9lw+fJl+Pr6yvZ1E0TUeIQQKCkpQUhISJP7FgTmFZG2uJJXTaoZu3z5MkJDQ9Uug4icdPHiRXTo0EHtMhTFvCLSJil51aSaMV9fXwC3f1F+fn4qV0NE9SkuLkZoaKj9vduUMK+ItMWVvNJUM7Z7924sXboUhw8fRmFhITZu3IiRI0c2ePk7h/r9/PwYbkQaosWP6ZhXRE2TlLzS1EkYN2/eRI8ePZCWlqZ2KUREdWJeEVFDaerI2NChQzF06NAGz19eXo7y8nL78+Li4sYoi4ioGuYVETWUpo6MOSs5ORn+/v72H54MS6Su0ooqdErajE5Jm1FaUaV2OW6FeUXkXpTMK103Y7NmzYLFYrH/XLx4Ue2SiIhqxLwiaro09TGls0wmE0wmk9plEBHVi3lF1HTp+sgYERERkbvT9ZExInIvHkYDxv4mzP6YiMhdKZlXmmrGbty4gbNnz9qf5+fn4+jRo2jVqhU6duyoYmVE1BAmTw8sHHm/2mUognlFpG1K5pWmmrFvvvkGAwYMsD9PTEwEACQkJCAjI0OlqoiIqmNeEVFDaaoZ69+/P4QQapdBRBIJIXDtZgUAoFVzb03eWb+hmFdE2qZkXmmqGSMibbtVaUWft3YCAE6++SiaeTOCiMg9KZlXvJqSiIiISEVsxoiIiIhUxGaMiIiISEVsxoiIiIhUxGaMiIiISEVsxoiIiIhUxOvKiUgxHkYDnurdwf6YiMhdKZlXbMaISDEmTw/8+ZkeapdBRFQvJfOKH1MSERERqYhHxohIMUII3Kq0AgB8vDx0/XVIRKRtSuYVj4wRkWJuVVpx77xtuHfeNnvIERG5IyXzis0YERERkYrYjBERERGpiM0YERERkYrYjBERERGpiM0YERERkYrYjBERERGpSFIzlpCQgN27d8tdCxHpnNFgwLDIYAyLDIZRoXuMMa+ISAol80rSTV8tFgtiY2MRFhaGCRMmICEhAe3bt5e7NiLSGbOXB/7yuz6KbpN5RURSKJlXko6Mbdq0Cf/+978xadIkrFu3Dp06dcLQoUOxYcMGVFZWyl0jEZFkzCsicneSzxkLDAxEYmIijh07hoMHDyIiIgJjx45FSEgIpk2bhjNnzshZJxGRZMwrInJnLp/AX1hYiB07dmDHjh3w8PDAsGHDcPz4cdx7771ITU2Vo0Yi0onSiip0StqMTkmbUVpRpfj2mVdE1FBK5pWkZqyyshJffPEFHnvsMYSFhWH9+vWYOnUqLl++jMzMTOzcuRNZWVl488035a6XiMgpzCsicneSTuBv164dbDYbxowZg0OHDqFnz57V5hkwYAACAgJcLI+IyDXMKyJyd5KasdTUVMTFxcFsNtc6T0BAAPLz8yUXRkQkB+YVEbk7pz+mrKysxIQJE3D27NnGqIeISDbMKyLSAqebMS8vL3Ts2BFWq7Ux6iEikg3zioi0QNIJ/HPmzMHs2bNx7do1ueshIpIV84qI3J2kc8aWL1+Os2fPIiQkBGFhYWjevLnD60eOHJGlOCLSF6PBgAHdAu2PlcC8IiIplMwrSc3YyJEjZS6DiJoCs5cH0idEKbpN5hURSaFkXhmEEEKRLbmB4uJi+Pv7w2KxwM/PT+1yiKgeTfk925THTqRFrrxnXb4DPxERERFJJ+ljSqvVitTUVGRlZaGgoAAVFRUOr/NEWSKqSWlFFfos3AkAODw3Fs28JUWQU5hXRCSFknkl6cjYggULkJKSgvj4eFgsFiQmJuLJJ5+E0WjEn/70J5lLJCI9uVVpxa1K5W41wbwiIqmUyitJzdinn36KVatWYfr06fD09MSYMWPw8ccfY968eThw4IDcNRIRSca8IiJ3J6kZu3LlCiIjIwEALVq0gMViAQA89thj2Lx5s3zVERG5iHlFRO5OUjPWoUMHFBYWAgC6dOmC7du3AwC+/vprmEwm+aojInIR84qI3J2kZmzUqFHIzs4GAEyZMgVz585F165dMW7cOPz+97+XtUAiIlcwr4jI3Um6NGDx4sX2x/Hx8ejYsSP279+Prl27YsSIEbIVR0TkKuYVEbk7Wa7TjI6ORnR0tByrIiIdMxoM6Bveyv5YDcwrImoIJfNKcjN25swZ7Nq1Cz/++CNsNpvDa/PmzXO5MCLSH7OXB9ZNVL4RYl4RkbOUzCtJzdiqVaswadIktGnTBsHBwTD8omM0GAwMNyJyG8wrInJ3kpqxt956C4sWLcLMmTPlroeISFbMKyJyd5Kupvz5558RFxcndy1EpHOlFVXovXAHei/cgdKKKkW2ybwiIimUzCtJR8bi4uKwfft2vPzyy3LX4xY6JVW/EWTqMz3Q1s8MCODqzXK09TUjKrwVPIy3P/Kw2gQO5V/DjyVl1V6ri9TlpFByW0rS67j0yGoTuHbz9ndDHjx/Df91V2Cj7yvmFfPKneh1XHqkZF5JasYiIiIwd+5cHDhwAJGRkfDy8nJ4/bXXXpOluJqkpaVh6dKluHLlCnr06IFly5YhKipKtvXXFGwAMC3rWLVp7fzNmD/iXgDAgv85iUJLWbXXhtzfrtZtbc0rlLScFEpuS0l6HZcebc0rxPy/n7A/n5DxtSL7inl1G/NKfXodlx4pnVcGIYRwdqHw8PDaV2gw4Pz58y4VVZt169Zh3Lhx+Oijj9C3b1+89957WL9+Pb777ju0bdu23uWLi4vh7+8Pi8UCPz+/aq/XFmy1MQCo7Zd3p3f+8LneNe64rXmFmPTJkWrL17ecFEpuS0l6HZceSd1X9b1nG4J5dRvzSl16HZceqZFXkpoxtfTt2xcPPvggli9fDgCw2WwIDQ3FlClTkJSUVO/ydf2inA22hjAACPY3Y8/MRxwObVptAg+98y+Hv44aspwUSm5LSXodlx65sq/kaMbUwrxynl7f13odlx6plVeSTuBXQ0VFBQ4fPozY2Fj7NKPRiNjYWOzfv7/GZcrLy1FcXOzwoyQBoNBShkP51xymH8q/VuuOrms5KZTclpL0Oi49aor7inkljV7/reh1XHqk1r5q8DljiYmJWLhwIZo3b47ExMQ6501JSXG5sF+7evUqrFYrgoKCHKYHBQXh9OnTNS6TnJyMBQsWyF6Ls34sKavzeUOXk2PbjbktJel1XHqkxr5iXknHvJKfXselR2rtqwY3Y7m5uaisrLQ/ro1Bpa84qcmsWbMcgri4uBihoaGK19HW11zn84YuJ8e2G3NbStLruPRIjX3FvJKOeSU/vY5Lj9TaVw1uxnbt2lXjY6W0adMGHh4eKCoqcpheVFSE4ODgGpcxmUwwmUxKlFejO58tR/3vd1vdERXeCu38zbhiKavxhNralpNCyW0pSa/j0iM19hXzynnMq8aj13HpkVr7SjPnjHl7e6NPnz7Izs62T7PZbMjOzpblS39/WDzc6WUMtTz+5fP5I+6tdpKfh9Fgv8TcmeWkUHJbStLruPSoKe4r5pU0ev23otdx6ZFa+0rS1ZSjRo2q8fC+wWCA2WxGREQEnn32WXTr1k2WIu9Yt24dEhISsGLFCkRFReG9995DVlYWTp8+Xe3cjJo05EoHZ65S4n171KfXcemRlH0lx9WUzKvbmFfq0+u49EjpvJLUjI0fPx6bNm1CQEAA+vTpAwA4cuQIrl+/jsGDB+PYsWP44YcfkJ2djd/+9rfOrr5Oy5cvt99EsWfPnvjggw/Qt2/fBi3b0F8U72itLXodlx7dKKtC/3d3wWYTSInviYe71n1HazmaMeYV88qd6HVceqRkXklqxpKSklBcXIzly5fDaLz9SafNZsPrr78OX19fLFq0CC+//DJOnDiBPXv2OLv6RqPlexYR6UFpRRXunbcNAHDyzUfRzLvu01bleM8yr4hICiXzStI5Y6tXr8bUqVPtwQbcvofOlClTsHLlShgMBrz66qvIy8uTsnoiItkwr4jI3Ulqxqqqqmq8V87p06dhtVoBAGaz2a0uGyeipol5RUTuTtIXhY8dOxbPP/88Zs+ejQcffBAA8PXXX+Ptt9/GuHHjAABfffUV7rvvPvkqJSKSgHlFRO5OUjOWmpqKoKAgLFmyxH4fnaCgIEybNg0zZ84EAAwePBhDhgyRr1IiIgmYV0Tk7iQ1Yx4eHpgzZw7mzJlj//60X5+s1rFjR9erIyJyEfOKiNydpGbsl3iVDxE1lAEGdG3bwv5YacwrImooJfNKcjO2YcMGZGVloaCgABUVFQ6vHTlyxOXCiEh/fLw9sCMxRvHtMq+IyFlK5pWkqyk/+OADTJgwAUFBQcjNzUVUVBRat26N8+fPY+jQoXLXSEQkGfOKiNydpGbsL3/5C1auXIlly5bB29sbb7zxBnbs2IHXXnsNFotF7hqJiCRjXhGRu5PUjBUUFKBfv34AAB8fH5SUlAC4fQn5559/Ll91RKQrtyqsGJTyFQalfIVbFVZFtsm8IiIplMwrSc1YcHAwrl27BuD2VUgHDhwAAOTn50PCtysRURMhIHDmxxs48+MNCCiTFcwrIpJCybyS1Iw98sgj+Pvf/w4AmDBhAqZNm4ZBgwYhPj4eo0aNkrVAIiJXMK+IyN1Juppy5cqVsNlsAIBXXnkFrVu3xr59+/D4449j4sSJshZIROQK5hURuTtJzZjRaHT40t3Ro0dj9OjRshVFRCQX5hURuTvJ9xkrKyvDt99+ix9//NH+V+cdjz/+uMuFERHJhXlFRO5MUjO2detWjBs3DlevXq32msFggNWqzFVSRET1YV4RkbuTdAL/lClTEBcXh8LCQthsNocfBhsR1cYAA9oH+KB9gI9iX4fEvCIiKZTMK4OQcG23n58fcnNz0aVLl8aoqdEUFxfD398fFouF31FHpAFyvGeZV0SkBFfes5KOjD399NPIycmRsigRkaKYV0Tk7iQdGSstLUVcXBwCAwMRGRkJLy8vh9dfe+012QqUE//SJNIWOd6zzCsiUoIr71lJJ/B//vnn2L59O8xmM3JycmAw/N9nqQaDwW3DjYjUVVZpxTMr9gMAsiZGw+zl0ejbZF4RkRRK5pWkZmzOnDlYsGABkpKSHO7fQ0RUF5sQ+PaSxf5YCcwrIpJCybySlEwVFRWIj49nsBGR22NeEZG7k5ROCQkJWLdundy1EBHJjnlFRO5O0seUVqsVS5YswbZt29C9e/dqJ8SmpKTIUhwRkauYV0Tk7iQ1Y8ePH0evXr0AAHl5ebIWREQkJ+YVEbk7Sc3Yrl275K6DiKhRMK+IyN051Yw9+eST9c5jMBjwxRdfSC6IiPStVXNvRbbDvCIiVymVV041Y/7+/o1VBxE1Ac28PXFk7iBFtsW8IiJXKJlXTjVj6enpjVUHEZGsmFdEpBW88Q4RERGRitiMEZFiyiqtiF+xH/Er9qOs0qp2OUREtVIyryRdTUlEJIVNCBzMv2Z/TETkrpTMKx4ZIyIiIlIRmzEiIiIiFbEZIyIiIlIRmzEiIiIiFbEZIyIiIlIRr6YkIkX5eHmoXQIRUYMolVdsxohIMc28PXFq4RC1yyAiqpeSecWPKYmIiIhUxGaMiIiISEVsxohIMWWVVkxIP4QJ6Yf4dUhE5NaUzCueM0ZEirEJgV3f/cf+mIjIXSmZVzwyRkRERKQiNmNEREREKtJMM7Zo0SL069cPzZo1Q0BAgNrlEBHVinlFRM7QTDNWUVGBuLg4TJo0Se1SiIjqxLwiImdo5gT+BQsWAAAyMjLULYSIqB7MKyJyhmaaMSnKy8tRXl5uf15cXKxiNUREtWNeETVdum7GkpOT7X+hEpH6mnl74ofFw9Uuwy0xr4jci5J5peo5Y0lJSTAYDHX+nD59WvL6Z82aBYvFYv+5ePGijNUTUVPCvCKixqLqkbHp06dj/Pjxdc7TuXNnyes3mUwwmUySlyciuoN5RUSNRdVmLDAwEIGBgWqWQETUIMwrImosmjlnrKCgANeuXUNBQQGsViuOHj0KAIiIiECLFi3ULY6I6BeYV0TkDM00Y/PmzUNmZqb9ea9evQAAu3btQv/+/Ru0DvG/3y3Fq5SItOHOe1Vo7HssmVdETY8reWUQWks5F1y6dAmhoaFql0FETrp48SI6dOigdhmKYl4RaZOUvGpSzZjNZsPly5fh6+sLg8FQ57zFxcUIDQ3FxYsX4efnp1CFjUuPYwI4Lq1xZlxCCJSUlCAkJARGo2a+MEQWzCv9jQnguLRGqbzSzMeUcjAajU53q35+frr6hwXoc0wAx6U1DR2Xv7+/AtW4H+bVbXocE8BxaU1j51XT+lOTiIiIyM2wGSMiIiJSEZuxWphMJsyfP19XN2HU45gAjktr9DouNenxd6rHMQEcl9YoNa4mdQI/ERERkbvhkTEiIiIiFbEZIyIiIlIRmzEiIiIiFbEZIyIiIlJRk2nG0tLS0KlTJ5jNZvTt2xeHDh2qc/7169fj7rvvhtlsRmRkJLZs2eLwuhAC8+bNQ7t27eDj44PY2FicOXOmMYdQI2fGtWrVKjz88MNo2bIlWrZsidjY2Grzjx8/HgaDweFnyJAhjT2MapwZV0ZGRrWazWazwzxa3F/9+/evNi6DwYDhw4fb51F7f+3evRsjRoxASEgIDAYDNm3aVO8yOTk56N27N0wmEyIiIpCRkVFtHmffr3rDvGJeaW1/Ma9czCvRBKxdu1Z4e3uLNWvWiBMnTogXX3xRBAQEiKKiohrn37t3r/Dw8BBLliwRJ0+eFH/84x+Fl5eXOH78uH2exYsXC39/f7Fp0yZx7Ngx8fjjj4vw8HBx69YtpYbl9LieffZZkZaWJnJzc8WpU6fE+PHjhb+/v7h06ZJ9noSEBDFkyBBRWFho/7l27ZpSQxJCOD+u9PR04efn51DzlStXHObR4v766aefHMaUl5cnPDw8RHp6un0etffXli1bxJw5c8Tf/vY3AUBs3LixzvnPnz8vmjVrJhITE8XJkyfFsmXLhIeHh9i6dat9Hmd/T3rDvLqNeaWt/cW8ci2vmkQzFhUVJV555RX7c6vVKkJCQkRycnKN8z/zzDNi+PDhDtP69u0rJk6cKIQQwmazieDgYLF06VL769evXxcmk0l8/vnnjTCCmjk7rl+rqqoSvr6+IjMz0z4tISFBPPHEE3KX6hRnx5Weni78/f1rXZ9e9ldqaqrw9fUVN27csE9zh/11R0PC7Y033hD33Xefw7T4+Hjx6KOP2p+7+nvSOuZVzZhX2tpfzCvn8kr3H1NWVFTg8OHDiI2NtU8zGo2IjY3F/v37a1xm//79DvMDwKOPPmqfPz8/H1euXHGYx9/fH3379q11nXKTMq5fKy0tRWVlJVq1auUwPScnB23btkW3bt0wadIk/PTTT7LWXhep47px4wbCwsIQGhqKJ554AidOnLC/ppf9tXr1aowePRrNmzd3mK7m/nJWfe8tOX5PWsa8qh3zSlv7i3nl3L7SfTN29epVWK1WBAUFOUwPCgrClStXalzmypUrdc5/57/OrFNuUsb1azNnzkRISIjDP6QhQ4bgr3/9K7Kzs/HOO+/gq6++wtChQ2G1WmWtvzZSxtWtWzesWbMGX375JT755BPYbDb069cPly5dAqCP/XXo0CHk5eXhhRdecJiu9v5yVm3vreLiYty6dUuWf9daxryqHfNKO/uLeeX8vvJ0uVrSpMWLF2Pt2rXIyclxOHl09OjR9seRkZHo3r07unTpgpycHAwcOFCNUusVHR2N6Oho+/N+/frhnnvuwYoVK7Bw4UIVK5PP6tWrERkZiaioKIfpWtxfRM5iXmkL88p5uj8y1qZNG3h4eKCoqMhhelFREYKDg2tcJjg4uM757/zXmXXKTcq47nj33XexePFibN++Hd27d69z3s6dO6NNmzY4e/asyzU3hCvjusPLywu9evWy16z1/XXz5k2sXbsWzz//fL3bUXp/Oau295afnx98fHxk2f9axryqjnnl/Dpdxby6Tcm80n0z5u3tjT59+iA7O9s+zWazITs72+Gvk1+Kjo52mB8AduzYYZ8/PDwcwcHBDvMUFxfj4MGDta5TblLGBQBLlizBwoULsXXrVjzwwAP1bufSpUv46aef0K5dO1nqro/Ucf2S1WrF8ePH7TVreX8Bt29bUF5ejueee67e7Si9v5xV33tLjv2vZcwrR8yr27SyvwDmleS8cup0f41au3atMJlMIiMjQ5w8eVK89NJLIiAgwH458dixY0VSUpJ9/r179wpPT0/x7rvvilOnTon58+fXeKl4QECA+PLLL8W3334rnnjiCVUuPXZmXIsXLxbe3t5iw4YNDpcWl5SUCCGEKCkpETNmzBD79+8X+fn5YufOnaJ3796ia9euoqyszG3HtWDBArFt2zZx7tw5cfjwYTF69GhhNpvFiRMnHMautf11x0MPPSTi4+OrTXeH/VVSUiJyc3NFbm6uACBSUlJEbm6uuHDhghBCiKSkJDF27Fj7/HcuFf/DH/4gTp06JdLS0mq8VLyu35PeMa/+r2bmlXb21x3MK2l51SSaMSGEWLZsmejYsaPw9vYWUVFR4sCBA/bXYmJiREJCgsP8WVlZ4q677hLe3t7ivvvuE5s3b3Z43Waziblz54qgoCBhMpnEwIEDxXfffafEUBw4M66wsDABoNrP/PnzhRBClJaWisGDB4vAwEDh5eUlwsLCxIsvvqjK/wSdGdfUqVPt8wYFBYlhw4aJI0eOOKxPi/tLCCFOnz4tAIjt27dXW5c77K9du3bV+G/qzjgSEhJETExMtWV69uwpvL29RefOnR3uQ3RHXb+npoB5xbzS2v4SgnnlSl4ZhBDCuWNpRERERCQX3Z8zRkREROTO2IwRERERqYjNGBEREZGK2IwRERERqYjNGBEREZGK2IwRERERqYjNGBEREZGK2IwRERERqYjNGDVJ48ePx8iRI9Uug4ioXswr/WMzRm5n/PjxMBgMMBgM8PLyQnh4ON544w2UlZWpXRoRkQPmFcnBU+0CiGoyZMgQpKeno7KyEocPH0ZCQgIMBgPeeecdtUsjInLAvCJX8cgYuSWTyYTg4GCEhoZi5MiRiI2NxY4dOwAANpsNycnJCA8Ph4+PD3r06IENGzbYl7VarXj++eftr3fr1g3vv/++WkMhIp1jXpGreGSM3F5eXh727duHsLAwAEBycjI++eQTfPTRR+jatSt2796N5557DoGBgYiJiYHNZkOHDh2wfv16tG7dGvv27cNLL72Edu3a4ZlnnlF5NESkZ8wrkoLNGLmlf/zjH2jRogWqqqpQXl4Oo9GI5cuXo7y8HG+//TZ27tyJ6OhoAEDnzp2xZ88erFixAjExMfDy8sKCBQvs6woPD8f+/fuRlZXFcCMi2TGvyFVsxsgtDRgwAB9++CFu3ryJ1NRUeHp64qmnnsKJEydQWlqKQYMGOcxfUVGBXr162Z+npaVhzZo1KCgowK1bt1BRUYGePXsqPAoiagqYV+QqNmPklpo3b46IiAgAwJo1a9CjRw+sXr0a999/PwBg8+bNaN++vcMyJpMJALB27VrMmDEDf/7znxEdHQ1fX18sXboUBw8eVHYQRNQkMK/IVWzGyO0ZjUbMnj0biYmJ+P7772EymVBQUICYmJga59+7dy/69euHyZMn26edO3dOqXKJqAljXpEUvJqSNCEuLg4eHh5YsWIFZsyYgWnTpiEzMxPnzp3DkSNHsGzZMmRmZgIAunbtim+++Qbbtm3D999/j7lz5+Lrr79WeQRE1FQwr8hZPDJGmuDp6YlXX30VS5YsQX5+PgIDA5GcnIzz588jICAAvXv3xuzZswEAEydORG5uLuLj42EwGDBmzBhMnjwZ//znP1UeBRE1BcwrcpZBCCHULoKIiIioqeLHlEREREQqYjNGREREpCI2Y0REREQqYjNGREREpCI2Y0REREQqYjNGREREpCI2Y0REREQqYjNGREREpCI2Y0REREQqYjNGREREpCI2Y6QZ/fv3R//+/dUug4ioXswrcgabMWo0GRkZMBgM9h+z2Yy77roLr776KoqKihSt5bPPPsN7770nadnr16+jbdu2MBgM2LBhg7yFEZFb0HJe9e/f36H2Oz9DhgxpvCJJVp5qF0D69+abbyI8PBxlZWXYs2cPPvzwQ2zZsgV5eXlo1qxZg9ezfft2yTV89tlnyMvLw9SpU51edt68eSgtLZW8bSLSDq3mVYcOHZCcnOwwLSQkRHINpCw2Y9Tohg4digceeAAA8MILL6B169ZISUnBl19+iTFjxjR4Pd7e3o1VYq3y8vLw4YcfYt68eZg3b57i2yciZWk1r/z9/fHcc88puk2SDz+mJMU98sgjAID8/HwAQFVVFRYuXIguXbrAZDKhU6dOmD17NsrLyx2W+/U5GDk5OTAYDMjKysKiRYvQoUMHmM1mDBw4EGfPnnVYbvPmzbhw4YL98H2nTp0aVOvrr7+OUaNG4eGHH3Zt0ESkSVrKq6qqKty4ccO1AZMqeGSMFHfu3DkAQOvWrQHc/uszMzMTTz/9NKZPn46DBw8iOTkZp06dwsaNG+td3+LFi2E0GjFjxgxYLBYsWbIEv/vd73Dw4EEAwJw5c2CxWHDp0iWkpqYCAFq0aFHvetevX499+/bh1KlT+OGHHySOloi0TCt59f3336N58+aoqKhAUFAQXnzxRcybNw9eXl5Sh04KYjNGjc5iseDq1asoKyvD3r178eabb8LHxwePPfYYjh07hszMTLzwwgtYtWoVAGDy5Mlo27Yt3n33XezatQsDBgyoc/1lZWU4evSo/WOBli1b4vXXX0deXh7uv/9+DBo0CO3bt8fPP//c4MP4t27dwowZMzBt2jR06tSJzRhRE6HFvOrSpQsGDBiAyMhI3Lx5Exs2bMBbb72F77//HuvWrXPtF0KK4MeU1OhiY2MRGBiI0NBQjB49Gi1atMDGjRvRvn17bNmyBQCQmJjosMz06dMBAJs3b653/RMmTHA4P+POR4rnz5+XXPPixYtRWVmJ2bNnS14HEWmPFvNq9erVmD9/Pp588kmMHTsWX375JV588UVkZWXhwIEDktdLyuGRMWp0aWlpuOuuu+Dp6YmgoCB069YNRuPtvwMuXLgAo9GIiIgIh2WCg4MREBCACxcu1Lv+jh07Ojxv2bIlAODnn3+WVO8PP/yApUuXIi0trUEfDxCRfmgtr2ozffp0rFq1Cjt37sRvfvMbWddN8mMzRo0uKirKfnVSbQwGg+T1e3h41DhdCCFpffPmzUP79u3Rv39/+8eTV65cAQD85z//wQ8//ICOHTvaA5qI9ENreVWb0NBQAMC1a9dkXS81DjZjpKqwsDDYbDacOXMG99xzj316UVERrl+/jrCwMFm240x4FhQU4OzZs+jcuXO11yZPngzg9l+xAQEBstRGRNrgjnlVmzsfewYGBrq8Lmp8/NOeVDVs2DAAqHa36ZSUFADA8OHDZdlO8+bNYbFYGjTvW2+9hY0bNzr8LFy4EADwxhtvYOPGjWjevLksdRGRdrhjXhUXF1e7rYYQAm+99RYA4NFHH5WlJmpcPDJGqurRowcSEhKwcuVKXL9+HTExMTh06BAyMzMxcuTIeq9Maqg+ffpg3bp1SExMxIMPPogWLVpgxIgRNc770EMPVZt25yjYgw8+iJEjR8pSExFpizvm1ZEjRzBmzBiMGTMGERERuHXrFjZu3Ii9e/fipZdeQu/evWWpiRoXmzFS3ccff4zOnTsjIyMDGzduRHBwMGbNmoX58+fLto3Jkyfj6NGjSE9PR2pqKsLCwmoNNyKi2rhbXoWFheHhhx/Gxo0bceXKFRiNRtxzzz346KOP8NJLL8lWEzUug5D7rEEiIiIiajCeM0ZERESkIjZjRERERCpiM0ZERESkIjZjRERERCpiM0ZERESkIjZjRERERCpqUvcZs9lsuHz5Mnx9fWX5ugkialxCCJSUlCAkJKTJfRco84pIW1zJqybVjF2+fNn+5alEpB0XL15Ehw4d1C5DUcwrIm2SkldNqhnz9fUFcPsX5efnp3I1RFSf4uJihIaG2t+7TQnzikhbXMkrTTVju3fvxtKlS3H48GEUFhZi48aNTn1P4J1D/X5+fgw3Ig3R4sd0zCuipklKXmnqJIybN2+iR48eSEtLU7sUIqI6Ma+IqKE0dWRs6NChGDp0aIPnLy8vR3l5uf15cXFxY5RFRFQN84qIGkpTR8aclZycDH9/f/sPT4YlUldpRRU6JW1Gp6TNKK2oUrsct8K8InIvSuaVrpuxWbNmwWKx2H8uXryodklERDViXhE1XZr6mNJZJpMJJpNJ7TKIiOrFvCJqunR9ZIyIiIjI3en6yBgRuRcPowFjfxNmf0xE5K6UzCtNNWM3btzA2bNn7c/z8/Nx9OhRtGrVCh07dlSxMiJqCJOnBxaOvF/tMhTBvCLSNiXzSlPN2DfffIMBAwbYnycmJgIAEhISkJGRoVJVRETVMa+IqKE01Yz1798fQgi1yyAiiYQQuHazAgDQqrm3Ju+s31DMKyJtUzKvNNWMEZG23aq0os9bOwEAJ998FM28GUFE5J6UzCteTUlERESkIjZjRERERCpiM0ZERESkIjZjRERERCpiM0ZERESkIjZjRERERCrideVEpBgPowFP9e5gf0xE5K6UzCs2Y0SkGJOnB/78TA+1yyAiqpeSecWPKYmIiIhUxCNjRKQYIQRuVVoBAD5eHrr+OiQi0jYl84pHxohIMbcqrbh33jbcO2+bPeSIiNyRknnFZoyIiIhIRWzGiIiIiFTEZoyIiIhIRWzGiIiIiFTEZoyIiIhIRWzGiIiIiFQkqRlLSEjA7t275a6FiHTOaDBgWGQwhkUGw6jQPcaYV0QkhZJ5JemmrxaLBbGxsQgLC8OECROQkJCA9u3by10bEemM2csDf/ldH0W3ybwiIimUzCtJR8Y2bdqEf//735g0aRLWrVuHTp06YejQodiwYQMqKyvlrpGISDLmFRG5O8nnjAUGBiIxMRHHjh3DwYMHERERgbFjxyIkJATTpk3DmTNn5KyTiEgy5hURuTOXT+AvLCzEjh07sGPHDnh4eGDYsGE4fvw47r33XqSmpspRIxHpRGlFFTolbUanpM0orahSfPvMKyJqKCXzSlIzVllZiS+++AKPPfYYwsLCsH79ekydOhWXL19GZmYmdu7ciaysLLz55pty10tE5BTmFRG5O0kn8Ldr1w42mw1jxozBoUOH0LNnz2rzDBgwAAEBAS6WR0TkGuYVEbk7Sc1Yamoq4uLiYDaba50nICAA+fn5kgsjIpID84qI3J3TH1NWVlZiwoQJOHv2bGPUQ0QkG+YVEWmB082Yl5cXOnbsCKvV2hj1EBHJhnlFRFog6QT+OXPmYPbs2bh27Zrc9RARyYp5RUTuTtI5Y8uXL8fZs2cREhKCsLAwNG/e3OH1I0eOyFIcEemL0WDAgG6B9sdKYF4RkRRK5pWkZmzkyJEyl0FETYHZywPpE6IU3SbzioikUDKvDEIIociW3EBxcTH8/f1hsVjg5+endjlEVI+m/J5tymMn0iJX3rMu34GfiIiIiKST9DGl1WpFamoqsrKyUFBQgIqKCofXeaIsEdWktKIKfRbuBAAcnhuLZt6SIsgpzCsikkLJvJJ0ZGzBggVISUlBfHw8LBYLEhMT8eSTT8JoNOJPf/qTzCUSkZ7cqrTiVqVyt5pgXhGRVErllaRm7NNPP8WqVaswffp0eHp6YsyYMfj4448xb948HDhwQO4aiYgkY14RkbuT1IxduXIFkZGRAIAWLVrAYrEAAB577DFs3rxZvuqIiFzEvCIidyepGevQoQMKCwsBAF26dMH27dsBAF9//TVMJpN81RERuYh5RUTuTlIzNmrUKGRnZwMApkyZgrlz56Jr164YN24cfv/738taIBGRK5hXROTuJF0asHjxYvvj+Ph4dOzYEfv370fXrl0xYsQI2YojInIV84qI3J0s12lGR0cjOjpajlURkY4ZDQb0DW9lf6wG5hURNYSSeSW5GTtz5gx27dqFH3/8ETabzeG1efPmuVwYEemP2csD6yYq3wgxr4jIWUrmlaRmbNWqVZg0aRLatGmD4OBgGH7RMRoMBoYbEbkN5hURuTtJzdhbb72FRYsWYebMmXLXQ0QkK+YVEbk7SVdT/vzzz4iLi5O7FiLSudKKKvReuAO9F+5AaUWVIttkXhGRFErmlaQjY3Fxcdi+fTtefvlluetxC52Sqt8IMvWZHmjrZwYEcPVmOdr6mhEV3goextsfeVhtAofyr+HHkrJqr9VF6nJSKLktJel1XHpktQlcu3n7uyEPnr+G/7orsNH3FfOKeeVO9DouPVIyryQ1YxEREZg7dy4OHDiAyMhIeHl5Obz+2muvyVJcTdLS0rB06VJcuXIFPXr0wLJlyxAVFSXb+msKNgCYlnWs2rR2/mbMH3EvAGDB/5xEoaWs2mtD7m9X67a25hVKWk4KJbelJL2OS4+25hVi/t9P2J9PyPhakX3FvLqNeaU+vY5Lj5TOK4MQQji7UHh4eO0rNBhw/vx5l4qqzbp16zBu3Dh89NFH6Nu3L9577z2sX78e3333Hdq2bVvv8sXFxfD394fFYoGfn1+112sLttoYANT2y7vTO3/4XO8ad9zWvEJM+uRIteXrW04KJbelJL2OS4+k7qv63rMNwby6jXmlLr2OS4/UyCtJzZha+vbtiwcffBDLly8HANhsNoSGhmLKlClISkqqd/m6flHOBltDGAAE+5uxZ+YjDoc2rTaBh975l8NfRw1ZTgolt6UkvY5Lj1zZV3I0Y2phXjlPr+9rvY5Lj9TKK0kn8KuhoqIChw8fRmxsrH2a0WhEbGws9u/fX+My5eXlKC4udvhRkgBQaCnDofxrDtMP5V+rdUfXtZwUSm5LSXodlx41xX3FvJJGr/9W9DouPVJrXzX4nLHExEQsXLgQzZs3R2JiYp3zpqSkuFzYr129ehVWqxVBQUEO04OCgnD69Okal0lOTsaCBQtkr8VZP5aU1fm8ocvJse3G3JaS9DouPVJjXzGvpGNeyU+v49IjtfZVg5ux3NxcVFZW2h/XxqDSV5zUZNasWQ5BXFxcjNDQUMXraOtrrvN5Q5eTY9uNuS0l6XVceqTGvmJeSce8kp9ex6VHau2rBjdju3btqvGxUtq0aQMPDw8UFRU5TC8qKkJwcHCNy5hMJphMJiXKq9Gdz5aj/ve7re6ICm+Fdv5mXLGU1XhCbW3LSaHktpSk13HpkRr7innlPOZV49HruPRIrX2lmXPGvL290adPH2RnZ9un2Ww2ZGdny/Klvz8sHu70MoZaHv/y+fwR91Y7yc/DaLBfYu7MclIouS0l6XVcetQU9xXzShq9/lvR67j0SK19JelqylGjRtV4eN9gMMBsNiMiIgLPPvssunXrJkuRd6xbtw4JCQlYsWIFoqKi8N577yErKwunT5+udm5GTRpypYMzVynxvj3q0+u49EjKvpLjakrm1W3MK/XpdVx6pHReSWrGxo8fj02bNiEgIAB9+vQBABw5cgTXr1/H4MGDcezYMfzwww/Izs7Gb3/7W2dXX6fly5fbb6LYs2dPfPDBB+jbt2+Dlm3oL4p3tNYWvY5Lj26UVaH/u7tgswmkxPfEw13rvqO1HM0Y84p55U70Oi49UjKvJDVjSUlJKC4uxvLly2E03v6k02az4fXXX4evry8WLVqEl19+GSdOnMCePXucXX2j0fI9i4j0oLSiCvfO2wYAOPnmo2jmXfdpq3K8Z5lXRCSFknkl6Zyx1atXY+rUqfZgA27fQ2fKlClYuXIlDAYDXn31VeTl5UlZPRGRbJhXROTuJDVjVVVVNd4r5/Tp07BarQAAs9nsVpeNE1HTxLwiIncn6YvCx44di+effx6zZ8/Ggw8+CAD4+uuv8fbbb2PcuHEAgK+++gr33XeffJUSEUnAvCIidyepGUtNTUVQUBCWLFliv49OUFAQpk2bhpkzZwIABg8ejCFDhshXKRGRBMwrInJ3kpoxDw8PzJkzB3PmzLF/f9qvT1br2LGj69UREbmIeUVE7k5SM/ZLvMqHiBrKAAO6tm1hf6w05hURNZSSeSW5GduwYQOysrJQUFCAiooKh9eOHDnicmFEpD8+3h7YkRij+HaZV0TkLCXzStLVlB988AEmTJiAoKAg5ObmIioqCq1bt8b58+cxdOhQuWskIpKMeUVE7k5SM/aXv/wFK1euxLJly+Dt7Y033ngDO3bswGuvvQaLxSJ3jUREkjGviMjdSWrGCgoK0K9fPwCAj48PSkpKANy+hPzzzz+Xrzoi0pVbFVYMSvkKg1K+wq0KqyLbZF4RkRRK5pWkZiw4OBjXrl0DcPsqpAMHDgAA8vPzIeHblYioiRAQOPPjDZz58QYElMkK5hURSaFkXklqxh555BH8/e9/BwBMmDAB06ZNw6BBgxAfH49Ro0bJWiARkSuYV0Tk7iRdTbly5UrYbDYAwCuvvILWrVtj3759ePzxxzFx4kRZCyQicgXziojcnaRmzGg0Onzp7ujRozF69GjZiiIikgvziojcneT7jJWVleHbb7/Fjz/+aP+r847HH3/c5cKIiOTCvCIidyapGdu6dSvGjRuHq1evVnvNYDDAalXmKikiovowr4jI3Uk6gX/KlCmIi4tDYWEhbDabww+DjYhqY4AB7QN80D7AR7GvQ2JeEZEUSuaVQUi4ttvPzw+5ubno0qVLY9TUaIqLi+Hv7w+LxcLvqCPSADnes8wrIlKCK+9ZSUfGnn76aeTk5EhZlIhIUcwrInJ3ko6MlZaWIi4uDoGBgYiMjISXl5fD66+99ppsBcqJf2kSaYsc71nmFREpwZX3rKQT+D///HNs374dZrMZOTk5MBj+77NUg8HgtuFGROoqq7TimRX7AQBZE6Nh9vJo9G0yr4hICiXzSlIzNmfOHCxYsABJSUkO9+8hIqqLTQh8e8lif6wE5hURSaFkXklKpoqKCsTHxzPYiMjtMa+IyN1JSqeEhASsW7dO7lqIiGTHvCIidyfpY0qr1YolS5Zg27Zt6N69e7UTYlNSUmQpjojIVcwrInJ3kpqx48ePo1evXgCAvLw8WQsiIpIT84qI3J2kZmzXrl1y10FE1CiYV0Tk7pxqxp588sl65zEYDPjiiy8kF0RE+taqubci22FeEZGrlMorp5oxf3//xqqDiJqAZt6eODJ3kCLbYl4RkSuUzCunmrH09PTGqoOISFbMKyLSCt54h4iIiEhFbMaISDFllVbEr9iP+BX7UVZpVbscIqJaKZlXkq6mJCKSwiYEDuZfsz8mInJXSuYVj4wRERERqYjNGBEREZGK2IwRERERqYjNGBEREZGK2IwRERERqYhXUxKRony8PNQugYioQZTKKzZjRKSYZt6eOLVwiNplEBHVS8m84seURERERCpiM0ZERESkIjZjRKSYskorJqQfwoT0Q/w6JCJya0rmFc8ZIyLF2ITAru/+Y39MROSulMwrHhkjIiIiUhGbMSIiIiIVaaYZW7RoEfr164dmzZohICBA7XKIiGrFvCIiZ2imGauoqEBcXBwmTZqkdilERHViXhGRMzRzAv+CBQsAABkZGeoWQkRUD+YVETlDM82YFOXl5SgvL7c/Ly4uVrEaIqLaMa+Imi5dN2PJycn2v1CJSH3NvD3xw+LhapfhlphXRO5FybxS9ZyxpKQkGAyGOn9Onz4tef2zZs2CxWKx/1y8eFHG6omoKWFeEVFjUfXI2PTp0zF+/Pg65+ncubPk9ZtMJphMJsnLExHdwbwiosaiajMWGBiIwMBANUsgImoQ5hURNRbNnDNWUFCAa9euoaCgAFarFUePHgUAREREoEWLFuoWR0T0C8wrInKGZpqxefPmITMz0/68V69eAIBdu3ahf//+DVqH+N/vluJVSkTacOe9KjT2PZbMK6Kmx5W8MgitpZwLLl26hNDQULXLICInXbx4ER06dFC7DEUxr4i0SUpeNalmzGaz4fLly/D19YXBYKhz3uLiYoSGhuLixYvw8/NTqMLGpccxARyX1jgzLiEESkpKEBISAqNRM18YIoumnleAPselxzEBHBfgWl5p5mNKORiNRqe7VT8/P139wwL0OSaA49Kaho7L399fgWrcD/Pq/+hxXHocE8BxSc2rpvWnJhEREZGbYTNGREREpCI2Y7UwmUyYP3++rm7CqMcxARyX1uh1XGrS6+9Uj+PS45gAjstVTeoEfiIiIiJ3wyNjRERERCpiM0ZERESkIjZjRERERCpiM0ZERESkoibTjKWlpaFTp04wm83o27cvDh06VOf869evx9133w2z2YzIyEhs2bLF4XUhBObNm4d27drBx8cHsbGxOHPmTGMOoUbOjGvVqlV4+OGH0bJlS7Rs2RKxsbHV5h8/fjwMBoPDz5AhQxp7GNU4M66MjIxqNZvNZod5tLi/+vfvX21cBoMBw4cPt8+j9v7avXs3RowYgZCQEBgMBmzatKneZXJyctC7d2+YTCZEREQgIyOj2jzOvl/1hnnFvNLa/mJeuZhXoglYu3at8Pb2FmvWrBEnTpwQL774oggICBBFRUU1zr93717h4eEhlixZIk6ePCn++Mc/Ci8vL3H8+HH7PIsXLxb+/v5i06ZN4tixY+Lxxx8X4eHh4tatW0oNy+lxPfvssyItLU3k5uaKU6dOifHjxwt/f39x6dIl+zwJCQliyJAhorCw0P5z7do1pYYkhHB+XOnp6cLPz8+h5itXrjjMo8X99dNPPzmMKS8vT3h4eIj09HT7PGrvry1btog5c+aIv/3tbwKA2LhxY53znz9/XjRr1kwkJiaKkydPimXLlgkPDw+xdetW+zzO/p70hnl1G/NKW/uLeeVaXjWJZiwqKkq88sor9udWq1WEhISI5OTkGud/5plnxPDhwx2m9e3bV0ycOFEIIYTNZhPBwcFi6dKl9tevX78uTCaT+PzzzxthBDVzdly/VlVVJXx9fUVmZqZ9WkJCgnjiiSfkLtUpzo4rPT1d+Pv717o+veyv1NRU4evrK27cuGGf5g77646GhNsbb7wh7rvvPodp8fHx4tFHH7U/d/X3pHXMq5oxr7S1v5hXzuWV7j+mrKiowOHDhxEbG2ufZjQaERsbi/3799e4zP79+x3mB4BHH33UPn9+fj6uXLniMI+/vz/69u1b6zrlJmVcv1ZaWorKykq0atXKYXpOTg7atm2Lbt26YdKkSfjpp59krb0uUsd148YNhIWFITQ0FE888QROnDhhf00v+2v16tUYPXo0mjdv7jBdzf3lrPreW3L8nrSMeVU75pW29hfzyrl9pftm7OrVq7BarQgKCnKYHhQUhCtXrtS4zJUrV+qc/85/nVmn3KSM69dmzpyJkJAQh39IQ4YMwV//+ldkZ2fjnXfewVdffYWhQ4fCarXKWn9tpIyrW7duWLNmDb788kt88sknsNls6NevHy5dugRAH/vr0KFDyMvLwwsvvOAwXe395aza3lvFxcW4deuWLP+utYx5VTvmlXb2F/PK+X3l6XK1pEmLFy/G2rVrkZOT43Dy6OjRo+2PIyMj0b17d3Tp0gU5OTkYOHCgGqXWKzo6GtHR0fbn/fr1wz333IMVK1Zg4cKFKlYmn9WrVyMyMhJRUVEO07W4v4icxbzSFuaV83R/ZKxNmzbw8PBAUVGRw/SioiIEBwfXuExwcHCd89/5rzPrlJuUcd3x7rvvYvHixdi+fTu6d+9e57ydO3dGmzZtcPbsWZdrbghXxnWHl5cXevXqZa9Z6/vr5s2bWLt2LZ5//vl6t6P0/nJWbe8tPz8/+Pj4yLL/tYx5VR3zyvl1uop5dZuSeaX7Zszb2xt9+vRBdna2fZrNZkN2drbDXye/FB0d7TA/AOzYscM+f3h4OIKDgx3mKS4uxsGDB2tdp9ykjAsAlixZgoULF2Lr1q144IEH6t3OpUuX8NNPP6Fdu3ay1F0fqeP6JavViuPHj9tr1vL+Am7ftqC8vBzPPfdcvdtRen85q773lhz7X8uYV46YV7dpZX8BzCvJeeXU6f4atXbtWmEymURGRoY4efKkeOmll0RAQID9cuKxY8eKpKQk+/x79+4Vnp6e4t133xWnTp0S8+fPr/FS8YCAAPHll1+Kb7/9VjzxxBOqXHrszLgWL14svL29xYYNGxwuLS4pKRFCCFFSUiJmzJgh9u/fL/Lz88XOnTtF7969RdeuXUVZWZnbjmvBggVi27Zt4ty5c+Lw4cNi9OjRwmw2ixMnTjiMXWv7646HHnpIxMfHV5vuDvurpKRE5ObmitzcXAFApKSkiNzcXHHhwgUhhBBJSUli7Nix9vnvXCr+hz/8QZw6dUqkpaXVeKl4Xb8nvWNe/V/NzCvt7K87mFfS8qpJNGNCCLFs2TLRsWNH4e3tLaKiosSBAwfsr8XExIiEhASH+bOyssRdd90lvL29xX333Sc2b97s8LrNZhNz584VQUFBwmQyiYEDB4rvvvtOiaE4cGZcYWFhAkC1n/nz5wshhCgtLRWDBw8WgYGBwsvLS4SFhYkXX3xRlf8JOjOuqVOn2ucNCgoSw4YNE0eOHHFYnxb3lxBCnD59WgAQ27dvr7Yud9hfu3btqvHf1J1xJCQkiJiYmGrL9OzZU3h7e4vOnTs73Ifojrp+T00B84p5pbX9JQTzypW8MgghhHPH0oiIiIhILro/Z4yIiIjInbEZIyIiIlIRmzEiIiIiFbEZIyIiIlIRmzEiIiIiFbEZIyIiIlIRmzEiIiIiFbEZIyIiIlIRmzFqksaPH4+RI0eqXQYRUb2YV/rHZozczvjx42EwGGAwGODl5YXw8HC88cYbKCsrU7s0IiIHzCuSg6faBRDVZMiQIUhPT0dlZSUOHz6MhIQEGAwGvPPOO2qXRkTkgHlFruKRMXJLJpMJwcHBCA0NxciRIxEbG4sdO3YAAGw2G5KTkxEeHg4fHx/06NEDGzZssC9rtVrx/PPP21/v1q0b3n//fbWGQkQ6x7wiV/HIGLm9vLw87Nu3D2FhYQCA5ORkfPLJJ/joo4/QtWtX7N69G8899xwCAwMRExMDm82GDh06YP369WjdujX27duHl156Ce3atcMzzzyj8miISM+YVyQFmzFyS//4xz/QokULVFVVoby8HEajEcuXL0d5eTnefvtt7Ny5E9HR0QCAzp07Y8+ePVixYgViYmLg5eWFBQsW2NcVHh6O/fv3Iysri+FGRLJjXpGr2IyRWxowYAA+/PBD3Lx5E6mpqfD09MRTTz2FEydOoLS0FIMGDXKYv6KiAr169bI/T0tLw5o1a1BQUIBbt26hoqICPXv2VHgURNQUMK/IVWzGyC01b94cERERAIA1a9agR48eWL16Ne6//34AwObNm9G+fXuHZUwmEwBg7dq1mDFjBv785z8jOjoavr6+WLp0KQ4ePKjsIIioSWBekavYjJHbMxqNmD17NhITE/H999/DZDKhoKAAMTExNc6/d+9e9OvXD5MnT7ZPO3funFLlElETxrwiKXg1JWlCXFwcPDw8sGLFCsyYMQPTpk1DZmYmzp07hyNHjmDZsmXIzMwEAHTt2hXffPMNtm3bhu+//x5z587F119/rfIIiKipYF6Rs3hkjDTB09MTr776KpYsWYL8/HwEBgYiOTkZ58+fR0BAAHr37o3Zs2cDACZOnIjc3FzEx8fDYDBgzJgxmDx5Mv75z3+qPAoiagqYV+QsgxBCqF0EERERUVPFjymJiIiIVMRmjIiIiEhFbMaIiIiIVMRmjIiIiEhFbMaIiIiIVMRmjIiIiEhFbMaIiIiIVMRmjIiIiEhFbMaIiIiIVMRmjIiIiEhFbMZIM/r374/+/furXQYRUb2YV+QMNmPUaDIyMmAwGOw/ZrMZd911F1599VUUFRUpWstnn32G9957z6llKioq8Pbbb+Puu++G2WxGUFAQhg8fjkuXLjVOkUSkGq3m1Q8//OBQ969/XnzxxcYtlmThqXYBpH9vvvkmwsPDUVZWhj179uDDDz/Eli1bkJeXh2bNmjV4Pdu3b5dcw2effYa8vDxMnTq1QfNXVlZi+PDh2LdvH1588UV0794dP//8Mw4ePAiLxYIOHTpIroWI3JfW8iowMBD//d//XW361q1b8emnn2Lw4MGS6yDlsBmjRjd06FA88MADAIAXXngBrVu3RkpKCr788kuMGTOmwevx9vZurBKrSU1NxVdffYU9e/YgKipKse0Skbq0llfNmzfHc889V216RkYG/Pz8MGLECEXqINfwY0pS3COPPAIAyM/PBwBUVVVh4cKF6NKlC0wmEzp16oTZs2ejvLzcYblfn4ORk5MDg8GArKwsLFq0CB06dIDZbMbAgQNx9uxZh+U2b96MCxcu2A/dd+rUqdb6bDYb3n//fYwaNQpRUVGoqqpCaWmpfL8AItIMd8+rmhQWFmLXrl148sknYTabpQ2cFMUjY6S4c+fOAQBat24N4PZfn5mZmXj66acxffp0HDx4EMnJyTh16hQ2btxY7/oWL14Mo9GIGTNmwGKxYMmSJfjd736HgwcPAgDmzJkDi8WCS5cuITU1FQDQokWLWtd38uRJXL58Gd27d8dLL72EzMxMVFRUIDIyEu+//z4GDBjg6q+AiDTC3fOqJmvXroXNZsPvfvc7p5YjFQmiRpKeni4AiJ07d4r//Oc/4uLFi2Lt2rWidevWwsfHR1y6dEkcPXpUABAvvPCCw7IzZswQAMS//vUv+7SYmBgRExNjf75r1y4BQNxzzz2ivLzcPv39998XAMTx48ft04YPHy7CwsIaVPff/vY3AUC0bt1adO3aVaSnp4v09HTRtWtX4e3tLY4dOybtF0JEbkureVWTPn36iHbt2gmr1Sp5HaQsfkxJjS42NhaBgYEIDQ3F6NGj0aJFC2zcuBHt27fHli1bAACJiYkOy0yfPh0AsHnz5nrXP2HCBIfzMx5++GEAwPnz5yXVe+PGDQBASUkJsrOzMX78eIwfPx47d+6EEAJLliyRtF4icn9ay6tf+/7773H48GGMHj0aRiP/F68V/JiSGl1aWhruuusueHp6IigoCN26dbOHxIULF2A0GhEREeGwTHBwMAICAnDhwoV619+xY0eH5y1btgQA/Pzzz5Lq9fHxAQD89re/RWhoqMN2HnroIezbt0/SeonI/Wktr37t008/BQB+RKkxbMao0UVFRdmvTqqNwWCQvH4PD48apwshJK0vJCQEABAUFFTttbZt2yI3N1fSeonI/Wktr37ts88+Q7du3dCnTx9Z1kfK4DFMUlVYWBhsNhvOnDnjML2oqAjXr19HWFiYLNtxJjwjIyPh5eWFf//739Veu3z5MgIDA2WpiYi0xR3z6pcOHjyIs2fP8qiYBrEZI1UNGzYMAKrdbTolJQUAMHz4cFm207x5c1gslgbN6+vri2HDhmHfvn04ffq0ffqpU6ewb98+DBo0SJaaiEhb3DGvfumzzz4DADz77LOy1EHK4ceUpKoePXogISEBK1euxPXr1xETE4NDhw4hMzMTI0eOlO02En369MG6deuQmJiIBx98EC1atKjzZohvv/02srOz8cgjj+C1114DAHzwwQdo1aoVZs+eLUtNRKQt7ppXAGC1WrFu3Tr85je/QZcuXWSpg5TDZoxU9/HHH6Nz587IyMjAxo0bERwcjFmzZmH+/PmybWPy5Mk4evQo0tPTkZqairCwsDrD7d5778VXX32FmTNn4q233oLRaMQjjzyCpUuXon379rLVRUTa4o55BQA7d+5EUVER5syZI1sdpByDkOusQSIiIiJyGs8ZIyIiIlIRmzEiIiIiFbEZIyIiIlIRmzEiIiIiFbEZIyIiIlIRmzEiIiIiFTWp+4zZbDZcvnwZvr6+Ln23GBEpQwiBkpIShISE2L+sualgXhFpiyt51aSascuXLyM0NFTtMojISRcvXkSHDh3ULkNRzCsibZKSV02qGfP19QVw+xfl5+encjVEVJ/i4mKEhoba37tNCfOKSFtcyStNNWO7d+/G0qVLcfjwYRQWFmLjxo0YOXJkg5e/c6jfz8+P4UakIVr8mI55RdQ0SckrTZ2EcfPmTfTo0QNpaWlql0JEVCfmFRE1lKaOjA0dOhRDhw5t8Pzl5eUoLy+3Py8uLm6MsoiIqmFeEVFDaerImLOSk5Ph7+9v/+HJsETqKq2oQqekzeiUtBmlFVVql+NWmFdE7kXJvNJ1MzZr1ixYLBb7z8WLF9UuiYioRswroqZLUx9TOstkMsFkMqldBhFRvZhXRE2Xro+MEREREbk7XR8ZIyL34mE0YOxvwuyPiYjclZJ5palm7MaNGzh79qz9eX5+Po4ePYpWrVqhY8eOKlZGRA1h8vTAwpH3q12GIphXRNqmZF5pqhn75ptvMGDAAPvzxMREAEBCQgIyMjJUqoqIqDrmFRE1lKaasf79+0MIoXYZRCSREALXblYAAFo199bknfUbinlFpG1K5pWmmjEi0rZblVb0eWsnAODkm4+imTcjiIjck5J5xaspiYiIiFTEZoyIiIhIRWzGiIiIiFTEZoyIiIhIRWzGiIiIiFTEZoyIiIhIRbyunIgU42E04KneHeyPiYjclZJ5xWaMiBRj8vTAn5/poXYZRET1UjKv+DElERERkYp4ZIyIFCOEwK1KKwDAx8tD11+HRETapmRe8cgYESnmVqUV987bhnvnbbOHHBGRO1Iyr9iMEREREamIzRgRERGRitiMEREREamIzRgRERGRitiMEREREamIzRgRERGRiiQ1YwkJCdi9e7fctRCRzhkNBgyLDMawyGAYFbrHGPOKiKRQMq8k3fTVYrEgNjYWYWFhmDBhAhISEtC+fXu5ayMinTF7eeAvv+uj6DaZV0QkhZJ5JenI2KZNm/Dvf/8bkyZNwrp169CpUycMHToUGzZsQGVlpdw1EhFJxrwiIncn+ZyxwMBAJCYm4tixYzh48CAiIiIwduxYhISEYNq0aThz5oycdRIRSca8IiJ35vIJ/IWFhdixYwd27NgBDw8PDBs2DMePH8e9996L1NRUOWokIp0orahCp6TN6JS0GaUVVYpvn3lFRA2lZF5JasYqKyvxxRdf4LHHHkNYWBjWr1+PqVOn4vLly8jMzMTOnTuRlZWFN998U+56iYicwrwiIncn6QT+du3awWazYcyYMTh06BB69uxZbZ4BAwYgICDAxfKIiFzDvCIidyepGUtNTUVcXBzMZnOt8wQEBCA/P19yYUREcmBeEZG7c/pjysrKSkyYMAFnz55tjHqIiGTDvCIiLXC6GfPy8kLHjh1htVobox4iItkwr4hICySdwD9nzhzMnj0b165dk7seIiJZMa+IyN1JOmds+fLlOHv2LEJCQhAWFobmzZs7vH7kyBFZiiMifTEaDBjQLdD+WAnMKyKSQsm8ktSMjRw5UuYyiKgpMHt5IH1ClKLbZF4RkRRK5pVBCCEU2ZIbKC4uhr+/PywWC/z8/NQuh4jq0ZTfs0157ERa5Mp71uU78BMRERGRdJI+prRarUhNTUVWVhYKCgpQUVHh8DpPlCWimpRWVKHPwp0AgMNzY9HMW1IEOYV5RURSKJlXko6MLViwACkpKYiPj4fFYkFiYiKefPJJGI1G/OlPf5K5RCLSk1uVVtyqVO5WE8wrIpJKqbyS1Ix9+umnWLVqFaZPnw5PT0+MGTMGH3/8MebNm4cDBw7IXSMRkWTMKyJyd5KasStXriAyMhIA0KJFC1gsFgDAY489hs2bN8tXHRGRi5hXROTuJDVjHTp0QGFhIQCgS5cu2L59OwDg66+/hslkkq86IiIXMa+IyN1JasZGjRqF7OxsAMCUKVMwd+5cdO3aFePGjcPvf/97WQskInIF84qI3J2kSwMWL15sfxwfH4+OHTti//796Nq1K0aMGCFbcURErmJeEZG7k+U6zejoaERHR8uxKiLSMaPBgL7hreyP1cC8IqKGUDKvJDdjZ86cwa5du/Djjz/CZrM5vDZv3jyXCyMi/TF7eWDdROUbIeYVETlLybyS1IytWrUKkyZNQps2bRAcHAzDLzpGg8HAcCMit8G8IiJ3J6kZe+utt7Bo0SLMnDlT7nqIiGTFvCIidyfpasqff/4ZcXFxctdCRDpXWlGF3gt3oPfCHSitqFJkm8wrIpJCybySdGQsLi4O27dvx8svvyx3PW6hU1L1G0GmPtMDbf3MgACu3ixHW18zosJbwcN4+yMPq03gUP41/FhSVu21ukhdTgolt6UkvY5Lj6w2gWs3b3835MHz1/BfdwU2+r5iXjGv3Ilex6VHSuaVpGYsIiICc+fOxYEDBxAZGQkvLy+H11977TVZiqtJWloali5diitXrqBHjx5YtmwZoqKiZFt/TcEGANOyjlWb1s7fjPkj7gUALPifkyi0lFV7bcj97Wrd1ta8QknLSaHktpSk13Hp0da8Qsz/+wn78wkZXyuyr5hXtzGv1KfXcemR0nllEEIIZxcKDw+vfYUGA86fP+9SUbVZt24dxo0bh48++gh9+/bFe++9h/Xr1+O7775D27Zt612+uLgY/v7+sFgs8PPzq/Z6bcFWGwOA2n55d3rnD5/rXeOO25pXiEmfHKm2fH3LSaHktpSk13HpkdR9Vd97tiGYV7cxr9Sl13HpkRp5JakZU0vfvn3x4IMPYvny5QAAm82G0NBQTJkyBUlJSfUuX9cvytlgawgDgGB/M/bMfMTh0KbVJvDQO/9y+OuoIctJoeS2lKTXcemRK/tKjmZMLcwr5+n1fa3XcemRWnkl6QR+NVRUVODw4cOIjY21TzMajYiNjcX+/ftrXKa8vBzFxcUOP0oSAAotZTiUf81h+qH8a7Xu6LqWk0LJbSlJr+PSo6a4r5hX0uj134pex6VHau2rBp8zlpiYiIULF6J58+ZITEysc96UlBSXC/u1q1evwmq1IigoyGF6UFAQTp8+XeMyycnJWLBggey1OOvHkrI6nzd0OTm23ZjbUpJex6VHauwr5pV0zCv56XVceqTWvmpwM5abm4vKykr749oYVPqKk5rMmjXLIYiLi4sRGhqqeB1tfc11Pm/ocnJsuzG3pSS9jkuP1NhXzCvpmFfy0+u49EitfdXgZmzXrl01PlZKmzZt4OHhgaKiIofpRUVFCA4OrnEZk8kEk8mkRHk1uvPZctT/frfVHVHhrdDO34wrlrIaT6itbTkplNyWkvQ6Lj1SY18xr5zHvGo8eh2XHqm1rzRzzpi3tzf69OmD7Oxs+zSbzYbs7GxZvvT3h8XDnV7GUMvjXz6fP+Leaif5eRgN9kvMnVlOCiW3pSS9jkuPmuK+Yl5Jo9d/K3odlx6pta8kXU05atSoGg/vGwwGmM1mRERE4Nlnn0W3bt1kKfKOdevWISEhAStWrEBUVBTee+89ZGVl4fTp09XOzahJQ650cOYqJd63R316HZceSdlXclxNyby6jXmlPr2OS4+UzitJzdj48eOxadMmBAQEoE+fPgCAI0eO4Pr16xg8eDCOHTuGH374AdnZ2fjtb3/r7OrrtHz5cvtNFHv27IkPPvgAffv2bdCyDf1F8Y7W2qLXcenRjbIq9H93F2w2gZT4nni4a913tJajGWNeMa/ciV7HpUdK5pWkZiwpKQnFxcVYvnw5jMbbn3TabDa8/vrr8PX1xaJFi/Dyyy/jxIkT2LNnj7OrbzRavmcRkR6UVlTh3nnbAAAn33wUzbzrPm1Vjvcs84qIpFAyrySdM7Z69WpMnTrVHmzA7XvoTJkyBStXroTBYMCrr76KvLw8KasnIpIN84qI3J2kZqyqqqrGe+WcPn0aVqsVAGA2m93qsnEiapqYV0Tk7iR9UfjYsWPx/PPPY/bs2XjwwQcBAF9//TXefvttjBs3DgDw1Vdf4b777pOvUiIiCZhXROTuJDVjqampCAoKwpIlS+z30QkKCsK0adMwc+ZMAMDgwYMxZMgQ+SolIpKAeUVE7k5SM+bh4YE5c+Zgzpw59u9P+/XJah07dnS9OiIiFzGviMjdSWrGfolX+RBRQxlgQNe2LeyPlca8IqKGUjKvJDdjGzZsQFZWFgoKClBRUeHw2pEjR1wujIj0x8fbAzsSYxTfLvOKiJylZF5Juprygw8+wIQJExAUFITc3FxERUWhdevWOH/+PIYOHSp3jUREkjGviMjdSWrG/vKXv2DlypVYtmwZvL298cYbb2DHjh147bXXYLFY5K6RiEgy5hURuTtJzVhBQQH69esHAPDx8UFJSQmA25eQf/755/JVR0S6cqvCikEpX2FQyle4VWFVZJvMKyKSQsm8ktSMBQcH49q1awBuX4V04MABAEB+fj4kfLsSETURAgJnfryBMz/egIAyWcG8IiIplMwrSc3YI488gr///e8AgAkTJmDatGkYNGgQ4uPjMWrUKFkLJCJyBfOKiNydpKspV65cCZvNBgB45ZVX0Lp1a+zbtw+PP/44Jk6cKGuBRESuYF4RkbuT1IwZjUaHL90dPXo0Ro8eLVtRRERyYV4RkbuTfJ+xsrIyfPvtt/jxxx/tf3Xe8fjjj7tcGBGRXJhXROTOJDVjW7duxbhx43D16tVqrxkMBlitylwlRURUH+YVEbk7SSfwT5kyBXFxcSgsLITNZnP4YbARUW0MMKB9gA/aB/go9nVIzCsikkLJvDIICdd2+/n5ITc3F126dGmMmhpNcXEx/P39YbFY+B11RBogx3uWeUVESnDlPSvpyNjTTz+NnJwcKYsSESmKeUVE7k7SkbHS0lLExcUhMDAQkZGR8PLycnj9tddek61AOfEvTSJtkeM9y7wiIiW48p6VdAL/559/ju3bt8NsNiMnJwcGw/99lmowGNw23IhIXWWVVjyzYj8AIGtiNMxeHo2+TeYVEUmhZF5JasbmzJmDBQsWICkpyeH+PUREdbEJgW8vWeyPlcC8IiIplMwrSclUUVGB+Ph4BhsRuT3mFRG5O0nplJCQgHXr1sldCxGR7JhXROTuJH1MabVasWTJEmzbtg3du3evdkJsSkqKLMUREbmKeUVE7k5SM3b8+HH06tULAJCXlydrQUREcmJeEZG7k9SM7dq1S+46iIgaBfOKiNydU83Yk08+We88BoMBX3zxheSCiEjfWjX3VmQ7zCsicpVSeeVUM+bv799YdRBRE9DM2xNH5g5SZFvMKyJyhZJ55VQzlp6e3lh1EBHJinlFRFrBG+8QERERqYjNGBEppqzSivgV+xG/Yj/KKq1ql0NEVCsl80rS1ZRERFLYhMDB/Gv2x0RE7krJvOKRMSIiIiIVsRkjIiIiUhGbMSIiIiIVsRkjIiIiUhGbMSIiIiIV8WpKIlKUj5eH2iUQETWIUnnFZoyIFNPM2xOnFg5RuwwionopmVf8mJKIiIhIRWzGiIiIiFTEZoyIFFNWacWE9EOYkH6IX4dERG5NybziOWNEpBibENj13X/sj4mI3JWSecUjY0REREQqYjNGREREpCLNNGOLFi1Cv3790KxZMwQEBKhdDhFRrZhXROQMzTRjFRUViIuLw6RJk9QuhYioTswrInKGZk7gX7BgAQAgIyND3UKIiOrBvCIiZ2imGZOivLwc5eXl9ufFxcUqVkNEVDvmFVHTpetmLDk52f4XKhGpr5m3J35YPFztMtwS84rIvSiZV6qeM5aUlASDwVDnz+nTpyWvf9asWbBYLPafixcvylg9ETUlzCsiaiyqHhmbPn06xo8fX+c8nTt3lrx+k8kEk8kkeXkiojuYV0TUWFRtxgIDAxEYGKhmCUREDcK8IqLGoplzxgoKCnDt2jUUFBTAarXi6NGjAICIiAi0aNFC3eKIiH6BeUVEztBMMzZv3jxkZmban/fq1QsAsGvXLvTv379B6xD/+91SvEqJSBvuvFeFxr7HknlF1PS4klcGobWUc8GlS5cQGhqqdhlE5KSLFy+iQ4cOapehKOYVkTZJyasm1YzZbDZcvnwZvr6+MBgMdc5bXFyM0NBQXLx4EX5+fgpV2Lj0OCaA49IaZ8YlhEBJSQlCQkJgNGrmC0Nk0dTzCtDnuPQ4JoDjAlzLK818TCkHo9HodLfq5+enq39YgD7HBHBcWtPQcfn7+ytQjfthXv0fPY5Lj2MCOC6pedW0/tQkIiIicjNsxoiIiIhUxGasFiaTCfPnz9fVTRj1OCaA49IavY5LTXr9nepxXHocE8BxuapJncBPRERE5G54ZIyIiIhIRWzGiIiIiFTEZoyIiIhIRWzGiIiIiFTUZJqxtLQ0dOrUCWazGX379sWhQ4fqnH/9+vW4++67YTabERkZiS1btji8LoTAvHnz0K5dO/j4+CA2NhZnzpxpzCHUyJlxrVq1Cg8//DBatmyJli1bIjY2ttr848ePh8FgcPgZMmRIYw+jGmfGlZGRUa1ms9nsMI8W91f//v2rjctgMGD48OH2edTeX7t378aIESMQEhICg8GATZs21btMTk4OevfuDZPJhIiICGRkZFSbx9n3q94wr5hXWttfzCsX80o0AWvXrhXe3t5izZo14sSJE+LFF18UAQEBoqioqMb59+7dKzw8PMSSJUvEyZMnxR//+Efh5eUljh8/bp9n8eLFwt/fX2zatEkcO3ZMPP744yI8PFzcunVLqWE5Pa5nn31WpKWlidzcXHHq1Ckxfvx44e/vLy5dumSfJyEhQQwZMkQUFhbaf65du6bUkIQQzo8rPT1d+Pn5OdR85coVh3m0uL9++uknhzHl5eUJDw8PkZ6ebp9H7f21ZcsWMWfOHPG3v/1NABAbN26sc/7z58+LZs2aicTERHHy5EmxbNky4eHhIbZu3Wqfx9nfk94wr25jXmlrfzGvXMurJtGMRUVFiVdeecX+3Gq1ipCQEJGcnFzj/M8884wYPny4w7S+ffuKiRMnCiGEsNlsIjg4WCxdutT++vXr14XJZBKff/55I4ygZs6O69eqqqqEr6+vyMzMtE9LSEgQTzzxhNylOsXZcaWnpwt/f/9a16eX/ZWamip8fX3FjRs37NPcYX/d0ZBwe+ONN8R9993nMC0+Pl48+uij9ueu/p60jnlVM+aVtvYX88q5vNL9x5QVFRU4fPgwYmNj7dOMRiNiY2Oxf//+GpfZv3+/w/wA8Oijj9rnz8/Px5UrVxzm8ff3R9++fWtdp9ykjOvXSktLUVlZiVatWjlMz8nJQdu2bdGtWzdMmjQJP/30k6y110XquG7cuIGwsDCEhobiiSeewIkTJ+yv6WV/rV69GqNHj0bz5s0dpqu5v5xV33tLjt+TljGvase80tb+Yl45t69034xdvXoVVqsVQUFBDtODgoJw5cqVGpe5cuVKnfPf+a8z65SblHH92syZMxESEuLwD2nIkCH461//iuzsbLzzzjv46quvMHToUFitVlnrr42UcXXr1g1r1qzBl19+iU8++QQ2mw39+vXDpUuXAOhjfx06dAh5eXl44YUXHKarvb+cVdt7q7i4GLdu3ZLl37WWMa9qx7zSzv5iXjm/rzxdrpY0afHixVi7di1ycnIcTh4dPXq0/XFkZCS6d++OLl26ICcnBwMHDlSj1HpFR0cjOjra/rxfv3645557sGLFCixcuFDFyuSzevVqREZGIioqymG6FvcXkbOYV9rCvHKe7o+MtWnTBh4eHigqKnKYXlRUhODg4BqXCQ4OrnP+O/91Zp1ykzKuO959910sXrwY27dvR/fu3euct3PnzmjTpg3Onj3rcs0N4cq47vDy8kKvXr3sNWt9f928eRNr167F888/X+92lN5fzqrtveXn5wcfHx9Z9r+WMa+qY145v05XMa9uUzKvdN+MeXt7o0+fPsjOzrZPs9lsyM7Odvjr5Jeio6Md5geAHTt22OcPDw9HcHCwwzzFxcU4ePBgreuUm5RxAcCSJUuwcOFCbN26FQ888EC927l06RJ++ukntGvXTpa66yN1XL9ktVpx/Phxe81a3l/A7dsWlJeX47nnnqt3O0rvL2fV996SY/9rGfPKEfPqNq3sL4B5JTmvnDrdX6PWrl0rTCaTyMjIECdPnhQvvfSSCAgIsF9OPHbsWJGUlGSff+/evcLT01O8++674tSpU2L+/Pk1XioeEBAgvvzyS/Htt9+KJ554QpVLj50Z1+LFi4W3t7fYsGGDw6XFJSUlQgghSkpKxIwZM8T+/ftFfn6+2Llzp+jdu7fo2rWrKCsrc9txLViwQGzbtk2cO3dOHD58WIwePVqYzWZx4sQJh7FrbX/d8dBDD4n4+Phq091hf5WUlIjc3FyRm5srAIiUlBSRm5srLly4IIQQIikpSYwdO9Y+/51Lxf/whz+IU6dOibS0tBovFa/r96R3zKv/q5l5pZ39dQfzSlpeNYlmTAghli1bJjp27Ci8vb1FVFSUOHDggP21mJgYkZCQ4DB/VlaWuOuuu4S3t7e47777xObNmx1et9lsYu7cuSIoKEiYTCYxcOBA8d133ykxFAfOjCssLEwAqPYzf/58IYQQpaWlYvDgwSIwMFB4eXmJsLAw8eKLL6ryP0FnxjV16lT7vEFBQWLYsGHiyJEjDuvT4v4SQojTp08LAGL79u3V1uUO+2vXrl01/pu6M46EhAQRExNTbZmePXsKb29v0blzZ4f7EN1R1++pKWBeMa+0tr+EYF65klcGIYRw7lgaEREREclF9+eMEREREbkzNmNEREREKmIzRkRERKQiNmNEREREKmIzRkRERKQiNmNEREREKmIzRkRERKQiNmNEREREKmIzRk3S+PHjMXLkSLXLICKqF/NK/9iMkdsZP348DAYDDAYDvLy8EB4ejjfeeANlZWVql0ZE5IB5RXLwVLsAopoMGTIE6enpqKysxOHDh5GQkACDwYB33nlH7dKIiBwwr8hVPDJGbslkMiE4OBihoaEYOXIkYmNjsWPHDgCAzWZDcnIywsPD4ePjgx49emDDhg32Za1WK55//nn76926dcP777+v1lCISOeYV+QqHhkjt5eXl4d9+/YhLCwMAJCcnIxPPvkEH330Ebp27Yrdu3fjueeeQ2BgIGJiYmCz2dChQwesX78erVu3xr59+/DSSy+hXbt2eOaZZ1QeDRHpGfOKpGAzRm7pH//4B1q0aIGqqiqUl5fDaDRi+fLlKC8vx9tvv42dO3ciOjoaANC5c2fs2bMHK1asQExMDLy8vLBgwQL7usLDw7F//35kZWUx3IhIdswrchWbMXJLAwYMwIcffoibN28iNTUVnp6eeOqpp3DixAmUlpZi0KBBDvNXVFSgV69e9udpaWlYs2YNCgoKcOvWLVRUVKBnz54Kj4KImgLmFbmKzRi5pebNmyMiIgIAsGbNGvTo0QOrV6/G/fffDwDYvHkz2rdv77CMyWQCAKxduxYzZszAn//8Z0RHR8PX1xdLly7FwYMHlR0EETUJzCtyFZsxcntGoxGzZ89GYmIivv/+e5hMJhQUFCAmJqbG+ffu3Yt+/fph8uTJ9mnnzp1TqlwiasKYVyQFr6YkTYiLi4OHhwdWrFiBGTNmYNq0acjMzMS5c+dw5MgRLFu2DJmZmQCArl274ptvvsG2bdvw/fffY+7cufj6669VHgERNRXMK3IWj4yRJnh6euLVV1/FkiVLkJ+fj8DAQCQnJ+P8+fMICAhA7969MXv2bADAxIkTkZubi/j4eBgMBowZMwaTJ0/GP//5T5VHQURNAfOKnGUQQgi1i/j/7d15fBR1nv/xdycknQA5BEMCEgLIsYLIpeEHK7+ARuXwwFEIzoiB8Rq8VgKrCAssIgOCA84AjuBIkhlHOVR0fouLHAOyItcAIuFwQJBjBRxFEhTI1d/fHyy9tiQhXelUdXdez8cjD7ur6/h8U/TbT6qrqgEAAOoqPqYEAABwEM0YAACAg2jGAAAAHEQzBgAA4CCaMQAAAAfRjAEAADiIZgwAAMBBNGMAAAAOohkDAABwEM0YAACAg2jGEDL69OmjPn36OF0GAFwWeQV/0Iyh1uTl5cnlcnl/YmJi1K5dOz3xxBM6efKkrbW8+eabevnll6s9v8fj0auvvqouXbqoYcOGSk5OVv/+/fXJJ5/UXpEAHBPKeVVaWqrJkyerdevWcrvdat26tV544QWVlZXVXpEIKL4oHLUmLy9PI0aM0PPPP69WrVrp/Pnz+vjjj/WnP/1JaWlpKigoUP369au9vpKSEklSdHS037XcfvvtKigo0Jdfflmt+UePHq1Zs2bp/vvvV+/evXX69GnNnz9fR44c0YYNG5Senu53DQCCVyjnVVZWlpYuXapf/vKXuv7667Vp0ybl5+fr4Ycf1oIFC/zePhxggFqSm5trJJmtW7f6TM/JyTGSzJtvvmlbLQMHDjRpaWnVmre0tNTExsaae++912f6wYMHjSTz1FNP1UKFAJwUqnm1ZcsWI8lMmDDBZ/ro0aONy+UyO3furIUKEWh8TAnb3XTTTZKkQ4cOSZLKyso0ZcoUXX311XK73WrZsqXGjRun4uJin+V+eg7GunXr5HK5tGTJEk2dOlXNmzdXTEyMbr75Zh04cMBnueXLl+vw4cPejyBatmxZaX2lpaU6d+6ckpOTfaY3adJEERERio2NreFvAECoCPa8+q//+i9J0tChQ32mDx06VMYYLV68uCbDh03qOV0A6p4vvvhCktS4cWNJ0kMPPaT8/Hzde++9Gj16tDZv3qxp06Zp7969WrZs2WXXN336dEVERGjMmDEqLCzUjBkz9Itf/EKbN2+WJI0fP16FhYU6duyYZs+eLUlq2LBhpeuLjY1Vjx49lJeXp549e3o/ppwyZYquuOIKPfLIIzX9FQAIEcGeVxebwJ/+kXjxI9Vt27b5OWI4wulDcwhfFw/7r1692vzjH/8wR48eNYsWLTKNGzc2sbGx5tixY+bTTz81ksxDDz3ks+yYMWOMJPPXv/7VOy0jI8NkZGR4n69du9ZIMtdcc40pLi72Tv/tb39rJJldu3Z5p/lz2N8YY/bv32+6detmJHl/Wrdubfbt2+f/LwJA0AvVvHrnnXeMJPOnP/3JZ/qrr75qJJlrr73Wj98CnMLHlKh1mZmZSkpKUmpqqoYOHaqGDRtq2bJluuqqq/TBBx9IknJycnyWGT16tCRp+fLll13/iBEjfE6S7d27tyTp4MGDlmuOi4tTx44d9fjjj+vdd9/VK6+8orKyMg0aNEjffPON5fUCCG6hllcDBgxQWlqaxowZo3fffVeHDx/WkiVLNH78eNWrV0/nzp2ztF7Yi48pUevmzZundu3aqV69ekpOTlb79u0VEXHh74DDhw8rIiJCbdq08VkmJSVFiYmJOnz48GXX36JFC5/nV1xxhSTpu+++s1RvWVmZMjMz1adPH82ZM8c7PTMzUx07dtTMmTP14osvWlo3gOAWankVExOj5cuXa8iQIbrnnnskSW63WzNmzNDUqVOr/IgTwYNmDLUuPT1d119/fZXzuFwuy+uPjIyscLqxeNeW9evXq6CgQLNmzfKZ3rZtW11zzTXasGGDpfUCCH6hlleS1LFjRxUUFGjPnj367rvv1KFDB8XGxmrUqFHKyMiwvF7Yh48p4ai0tDR5PB7t37/fZ/rJkyd1+vRppaWlBWQ7/oTnxRs8lpeXX/JaaWkpN1IE6qhgzKsfL9OxY0fdeOONatSokdauXSuPx6PMzMyA1ITaRTMGRw0YMECSLrnb9MWjUgMHDgzIdho0aKDCwsJqzduuXTtJ0qJFi3ymb9++XZ9//rm6du0akJoAhJZgzKuKnDt3ThMmTFDTpk113333BaQm1C4+poSjOnfurOzsbC1YsECnT59WRkaGtmzZovz8fA0aNEh9+/YNyHa6d++uxYsXKycnRzfccIMaNmyoO+64o9J5b7nlFuXn56uoqEi33nqrjh8/rjlz5ig2NlZPP/10QGoCEFqCMa8kaciQIWrWrJk6dOigoqIiLVy4UAcPHtTy5csVFxcXkJpQu2jG4Lg//OEPat26tfLy8rRs2TKlpKToueee06RJkwK2jccee0yffvqpcnNzNXv2bKWlpVUZbu+//75eeuklLVq0SCtWrFB0dLR69+6tKVOmqH379gGrC0BoCca8uv7665Wbm6v58+crNjZWvXv31ptvvqkuXboErCbULr6bEgAAwEGcMwYAAOAgmjEAAAAH0YwBAAA4iGYMAADAQTRjAAAADqIZAwAAcFCdus+Yx+PRV199pbi4uBp9txgAexhjdObMGTVr1sz7Zc11BXkFhJaa5FWdasa++uorpaamOl0GAD8dPXpUzZs3d7oMW5FXQGiykld1qhm7+LUQR48eVXx8vMPVALicoqIipaam1smvdCGvgNBSk7wKqWZs/fr1mjlzprZt26bjx49r2bJlGjRoULWXv3ioPz4+nnADQkgofkxHXgF1k5W8CqmTMH744Qd17txZ8+bNc7oUAKgSeQWgukLqyFj//v3Vv3//as9fXFys4uJi7/OioqLaKAsALkFeAaiukDoy5q9p06YpISHB+8PJsICzzpaUqeXY5Wo5drnOlpQ5XU5QIa+A4GJnXoV1M/bcc8+psLDQ+3P06FGnSwKACpFXQN0VUh9T+svtdsvtdjtdBgBcFnkF1F1hfWQMAAAg2IX1kTEAwSUywqVh/yfN+xgAgpWdeRVSzdj333+vAwcOeJ8fOnRIn376qRo1aqQWLVo4WBmA6nDXi9SUQdc6XYYtyCsgtNmZVyHVjP3tb39T3759vc9zcnIkSdnZ2crLy3OoKgC4FHkFoLpCqhnr06ePjDFOlwHAImOMTv1QIklq1CA6JO+sX13kFRDa7MyrkGrGAIS2c6Xl6v7CaknSnudvU/1oIghAcLIzr7iaEgAAwEE0YwAAAA6iGQMAAHAQzRgAAICDaMYAAAAcRDMGAADgIK4rB2CbyAiX7unW3PsYAIKVnXlFMwbANu56kfrNkM5OlwEAl2VnXvExJQAAgIM4MgbANsYYnSstlyTFRkWG9dchAQhtduYVR8YA2OZcabk6TPxQHSZ+6A05AAhGduYVzRgAAICDaMYAAAAcRDMGAADgIJoxAAAAB9GMAQAAOIhmDAAAwEGWmrHs7GytX78+0LUACHMRLpcGdErRgE4pirDpHmPkFQAr7MwrSzd9LSwsVGZmptLS0jRixAhlZ2frqquuCnRtAMJMTFSkXvlFd1u3SV4BsMLOvLJ0ZOy9997Tf//3f2vkyJFavHixWrZsqf79++vtt99WaWlpoGsEAMvIKwDBzvI5Y0lJScrJydHOnTu1efNmtWnTRsOGDVOzZs00atQo7d+/P5B1AoBl5BWAYFbjE/iPHz+uVatWadWqVYqMjNSAAQO0a9cudejQQbNnzw5EjQDCxNmSMrUcu1wtxy7X2ZIy27dPXgGoLjvzylIzVlpaqnfeeUe333670tLStHTpUj399NP66quvlJ+fr9WrV2vJkiV6/vnnA10vAPiFvAIQ7CydwN+0aVN5PB7dd9992rJli7p06XLJPH379lViYmINywOAmiGvAAQ7S83Y7NmzNXjwYMXExFQ6T2Jiog4dOmS5MAAIBPIKQLDz+2PK0tJSjRgxQgcOHKiNegAgYMgrAKHA72YsKipKLVq0UHl5eW3UAwABQ14BCAWWTuAfP368xo0bp1OnTgW6HgAIKPIKQLCzdM7Y3LlzdeDAATVr1kxpaWlq0KCBz+vbt28PSHEAwkuEy6W+7ZO8j+1AXgGwws68stSMDRo0KMBlAKgLYqIilTsi3dZtklcArLAzr1zGGGPLloJAUVGREhISVFhYqPj4eKfLAXAZdfk9W5fHDoSimrxna3wHfgAAAFhn6WPK8vJyzZ49W0uWLNGRI0dUUlLi8zonygKoyNmSMnWfslqStG1CpupHW4ogv5BXAKywM68sHRmbPHmyZs2apaysLBUWFionJ0c/+9nPFBERoX//938PcIkAwsm50nKdK7XvVhPkFQCr7MorS83Yn//8Z7322msaPXq06tWrp/vuu09/+MMfNHHiRG3atCnQNQKAZeQVgGBnqRk7ceKEOnXqJElq2LChCgsLJUm33367li9fHrjqAKCGyCsAwc5SM9a8eXMdP35cknT11Vdr5cqVkqStW7fK7XYHrjoAqCHyCkCws9SM3X333VqzZo0k6cknn9SECRPUtm1bPfDAA/rlL38Z0AIBoCbIKwDBztKlAdOnT/c+zsrKUosWLbRx40a1bdtWd9xxR8CKA4CaIq8ABLuAXKfZs2dP9ezZMxCrAhDGIlwu9WjVyPvYCeQVgOqwM68sN2P79+/X2rVr9fXXX8vj8fi8NnHixBoXBiD8xERFavGj9jdC5BUAf9mZV5aasddee00jR47UlVdeqZSUFLl+1DG6XC7CDUDQIK8ABDtLzdgLL7ygqVOn6tlnnw10PQAQUOQVgGBn6WrK7777ToMHDw50LQDC3NmSMnWbskrdpqzS2ZIyW7ZJXgGwws68snRkbPDgwVq5cqV+9atfBbqeoNBy7KU3gpw9pLOaxMdIRvrmh2I1iYtReqtGioy48JFHucdoy6FT+vrM+Uteq4rV5aywc1t2CtdxhaNyj9GpHy58N+Tmg6f0f9sl1fq+Iq/Iq2ASruMKR3bmlaVmrE2bNpowYYI2bdqkTp06KSoqyuf1p556KiDFVWTevHmaOXOmTpw4oc6dO2vOnDlKT08P2PorCjZJGrVk5yXTmibEaNIdHSRJk//fHh0vPH/Ja/2ubVrptlYUHLe0nBV2bstO4TqucLSi4Lgm/WW39/mIvK227Cvy6gLyynnhOq5wZHdeuYwxxt+FWrVqVfkKXS4dPHiwRkVVZvHixXrggQf06quvqkePHnr55Ze1dOlSff7552rSpMllly8qKlJCQoIKCwsVHx9/yeuVBVtlXJIq++Vd7J1/f3+3CnfcioLjGvnG9kuWv9xyVti5LTuF67jCkdV9dbn3bHWQVxeQV84K13GFIyfyylIz5pQePXrohhtu0Ny5cyVJHo9HqampevLJJzV27NjLLl/VL8rfYKsOl6SUhBh9/OxNPoc2yz1GN774V5+/jqqznBV2bstO4TqucFSTfRWIZswp5JX/wvV9Ha7jCkdO5ZWlE/idUFJSom3btikzM9M7LSIiQpmZmdq4cWOFyxQXF6uoqMjnx05G0vHC89py6JTP9C2HTlW6o6tazgo7t2WncB1XOKqL+4q8siZc/62E67jCkVP7qtrnjOXk5GjKlClq0KCBcnJyqpx31qxZNS7sp7755huVl5crOTnZZ3pycrL27dtX4TLTpk3T5MmTA16Lv74+c77K59VdLhDbrs1t2SlcxxWOnNhX5JV15FXgheu4wpFT+6razdiOHTtUWlrqfVwZl0NfcVKR5557zieIi4qKlJqaansdTeJiqnxe3eUCse3a3JadwnVc4ciJfUVeWUdeBV64jiscObWvqt2MrV27tsLHdrnyyisVGRmpkydP+kw/efKkUlJSKlzG7XbL7XbbUV6FLn62nP4/3211UXqrRmqaEKMThecrPKG2suWssHNbdgrXcYUjJ/YVeeU/8qr2hOu4wpFT+ypkzhmLjo5W9+7dtWbNGu80j8ejNWvWBORLf7+cPtDvZVyVPP7x80l3dLjkJL/ICJf3EnN/lrPCzm3ZKVzHFY7q4r4ir6wJ138r4TqucOTUvrJ0NeXdd99d4eF9l8ulmJgYtWnTRj//+c/Vvn37gBR50eLFi5Wdna358+crPT1dL7/8spYsWaJ9+/Zdcm5GRapzpYM/Vylx3x7nheu4wpGVfRWIqynJqwvIK+eF67jCkd15ZakZGz58uN577z0lJiaqe/fukqTt27fr9OnTuvXWW7Vz5059+eWXWrNmjf75n//Z39VXae7cud6bKHbp0kW/+93v1KNHj2otW91fFHe0Di3hOq5w9P35MvV5aa08HqNZWV3Uu23Vd7QORDNGXpFXwSRcxxWO7MwrS83Y2LFjVVRUpLlz5yoi4sInnR6PR//yL/+iuLg4TZ06Vb/61a+0e/duffzxx/6uvtaE8j2LgHBwtqRMHSZ+KEna8/xtqh9d9WmrgXjPklcArLAzryydM/b666/r6aef9gabdOEeOk8++aQWLFggl8ulJ554QgUFBVZWDwABQ14BCHaWmrGysrIK75Wzb98+lZeXS5JiYmKC6rJxAHUTeQUg2Fn6ovBhw4bpwQcf1Lhx43TDDTdIkrZu3apf//rXeuCBByRJH330kTp27Bi4SgHAAvIKQLCz1IzNnj1bycnJmjFjhvc+OsnJyRo1apSeffZZSdKtt96qfv36Ba5SALCAvAIQ7Cw1Y5GRkRo/frzGjx/v/f60n56s1qJFi5pXBwA1RF4BCHaWmrEf4yofANXlkkttmzT0PrYbeQWguuzMK8vN2Ntvv60lS5boyJEjKikp8Xlt+/btNS4MQPiJjY7UqpwM27dLXgHwl515Zelqyt/97ncaMWKEkpOTtWPHDqWnp6tx48Y6ePCg+vfvH+gaAcAy8gpAsLPUjL3yyitasGCB5syZo+joaD3zzDNatWqVnnrqKRUWFga6RgCwjLwCEOwsNWNHjhxRr169JEmxsbE6c+aMpAuXkL/11luBqw5AWDlXUq5bZn2kW2Z9pHMl5bZsk7wCYIWdeWWpGUtJSdGpU6ckXbgKadOmTZKkQ4cOycK3KwGoI4yM9n/9vfZ//b2M7MkK8gqAFXbmlaVm7KabbtJf/vIXSdKIESM0atQo3XLLLcrKytLdd98d0AIBoCbIKwDBztLVlAsWLJDH45EkPf7442rcuLE++eQT3XnnnXr00UcDWiAA1AR5BSDYWWrGIiIifL50d+jQoRo6dGjAigKAQCGvAAQ7y/cZO3/+vD777DN9/fXX3r86L7rzzjtrXBgABAp5BSCYWWrGVqxYoQceeEDffPPNJa+5XC6Vl9tzlRQAXA55BSDYWTqB/8knn9TgwYN1/PhxeTwenx+CDUBlXHLpqsRYXZUYa9vXIZFXAKywM69cxsK13fHx8dqxY4euvvrq2qip1hQVFSkhIUGFhYV8Rx0QAgLxniWvANihJu9ZS0fG7r33Xq1bt87KogBgK/IKQLCzdGTs7NmzGjx4sJKSktSpUydFRUX5vP7UU08FrMBA4i9NILQE4j1LXgGwQ03es5ZO4H/rrbe0cuVKxcTEaN26dXK5/vezVJfLFbThBsBZ50vLNWT+RknSkkd7KiYqsta3SV4BsMLOvLLUjI0fP16TJ0/W2LFjfe7fAwBV8Rijz44Veh/bgbwCYIWdeWUpmUpKSpSVlUWwAQh65BWAYGcpnbKzs7V48eJA1wIAAUdeAQh2lj6mLC8v14wZM/Thhx/quuuuu+SE2FmzZgWkOACoKfIKQLCz1Izt2rVLXbt2lSQVFBQEtCAACCTyCkCws9SMrV27NtB1AECtIK8ABDu/mrGf/exnl53H5XLpnXfesVwQgPDWqEG0LdshrwDUlF155VczlpCQUFt1AKgD6kfX0/YJt9iyLfIKQE3YmVd+NWO5ubm1VQcABBR5BSBUcOMdAAAAB9GMAbDN+dJyZc3fqKz5G3W+tNzpcgCgUnbmlaWrKQHACo8x2nzolPcxAAQrO/OKI2MAAAAOohkDAABwEM0YAACAg2jGAAAAHEQzBgAA4CCupgRgq9ioSKdLAIBqsSuvaMYA2KZ+dD3tndLP6TIA4LLszCs+pgQAAHAQzRgAAICDaMYA2OZ8ablG5G7RiNwtfB0SgKBmZ15xzhgA23iM0drP/+F9DADBys684sgYAACAg2jGAAAAHBQyzdjUqVPVq1cv1a9fX4mJiU6XAwCVIq8A+CNkmrGSkhINHjxYI0eOdLoUAKgSeQXAHyFzAv/kyZMlSXl5ec4WAgCXQV4B8EfINGNWFBcXq7i42Pu8qKjIwWoAoHLkFVB3hXUzNm3aNO9fqACcVz+6nr6cPtDpMoISeQUEFzvzytFzxsaOHSuXy1Xlz759+yyv/7nnnlNhYaH35+jRowGsHkBdQl4BqC2OHhkbPXq0hg8fXuU8rVu3trx+t9stt9tteXkAuIi8AlBbHG3GkpKSlJSU5GQJAFAt5BWA2hIy54wdOXJEp06d0pEjR1ReXq5PP/1UktSmTRs1bNjQ2eIA4EfIKwD+CJlmbOLEicrPz/c+79q1qyRp7dq16tOnT7XWYf7nu6W4SgkIDRffqybEvseSvALqnprklcuEWsrVwLFjx5Samup0GQD8dPToUTVv3tzpMmxFXgGhyUpe1almzOPx6KuvvlJcXJxcLleV8xYVFSk1NVVHjx5VfHy8TRXWrnAck8S4Qo0/4zLG6MyZM2rWrJkiIkLmC0MCoq7nlRSe4wrHMUmMS6pZXoXMx5SBEBER4Xe3Gh8fH1b/sKTwHJPEuEJNdceVkJBgQzXBh7z6X+E4rnAck8S4rOZV3fpTEwAAIMjQjAEAADiIZqwSbrdbkyZNCqubMIbjmCTGFWrCdVxOCtffaTiOKxzHJDGumqpTJ/ADAAAEG46MAQAAOIhmDAAAwEE0YwAAAA6iGQMAAHBQnWnG5s2bp5YtWyomJkY9evTQli1bqpx/6dKl+qd/+ifFxMSoU6dO+uCDD3xeN8Zo4sSJatq0qWJjY5WZman9+/fX5hAq5M+4XnvtNfXu3VtXXHGFrrjiCmVmZl4y//Dhw+VyuXx++vXrV9vDuIQ/48rLy7uk5piYGJ95QnF/9enT55JxuVwuDRw40DuP0/tr/fr1uuOOO9SsWTO5XC699957l11m3bp16tatm9xut9q0aaO8vLxL5vH3/RpuyCvyKtT2F3lVw7wydcCiRYtMdHS0Wbhwodm9e7d5+OGHTWJiojl58mSF82/YsMFERkaaGTNmmD179ph/+7d/M1FRUWbXrl3eeaZPn24SEhLMe++9Z3bu3GnuvPNO06pVK3Pu3Dm7huX3uH7+85+befPmmR07dpi9e/ea4cOHm4SEBHPs2DHvPNnZ2aZfv37m+PHj3p9Tp07ZNSRjjP/jys3NNfHx8T41nzhxwmeeUNxf3377rc+YCgoKTGRkpMnNzfXO4/T++uCDD8z48ePNu+++aySZZcuWVTn/wYMHTf369U1OTo7Zs2ePmTNnjomMjDQrVqzwzuPv7ynckFcXkFehtb/Iq5rlVZ1oxtLT083jjz/ufV5eXm6aNWtmpk2bVuH8Q4YMMQMHDvSZ1qNHD/Poo48aY4zxeDwmJSXFzJw50/v66dOnjdvtNm+99VYtjKBi/o7rp8rKykxcXJzJz8/3TsvOzjZ33XVXoEv1i7/jys3NNQkJCZWuL1z21+zZs01cXJz5/vvvvdOCYX9dVJ1we+aZZ0zHjh19pmVlZZnbbrvN+7ymv6dQR15VjLwKrf1FXvmXV2H/MWVJSYm2bdumzMxM77SIiAhlZmZq48aNFS6zceNGn/kl6bbbbvPOf+jQIZ04ccJnnoSEBPXo0aPSdQaalXH91NmzZ1VaWqpGjRr5TF+3bp2aNGmi9u3ba+TIkfr2228DWntVrI7r+++/V1pamlJTU3XXXXdp9+7d3tfCZX+9/vrrGjp0qBo0aOAz3cn95a/LvbcC8XsKZeRV5cir0Npf5JV/+yrsm7FvvvlG5eXlSk5O9pmenJysEydOVLjMiRMnqpz/4n/9WWegWRnXTz377LNq1qyZzz+kfv366Y9//KPWrFmjF198UR999JH69++v8vLygNZfGSvjat++vRYuXKj3339fb7zxhjwej3r16qVjx45JCo/9tWXLFhUUFOihhx7yme70/vJXZe+toqIinTt3LiD/rkMZeVU58ip09hd55f++qlfjahGSpk+frkWLFmndunU+J48OHTrU+7hTp0667rrrdPXVV2vdunW6+eabnSj1snr27KmePXt6n/fq1UvXXHON5s+frylTpjhYWeC8/vrr6tSpk9LT032mh+L+AvxFXoUW8sp/YX9k7Morr1RkZKROnjzpM/3kyZNKSUmpcJmUlJQq57/4X3/WGWhWxnXRSy+9pOnTp2vlypW67rrrqpy3devWuvLKK3XgwIEa11wdNRnXRVFRUeratau35lDfXz/88IMWLVqkBx988LLbsXt/+auy91Z8fLxiY2MDsv9DGXl1KfLK/3XWFHl1gZ15FfbNWHR0tLp37641a9Z4p3k8Hq1Zs8bnr5Mf69mzp8/8krRq1Srv/K1atVJKSorPPEVFRdq8eXOl6ww0K+OSpBkzZmjKlClasWKFrr/++stu59ixY/r222/VtGnTgNR9OVbH9WPl5eXatWuXt+ZQ3l/ShdsWFBcX6/7777/sduzeX/663HsrEPs/lJFXvsirC0Jlf0nkleW88ut0/xC1aNEi43a7TV5entmzZ4955JFHTGJiovdy4mHDhpmxY8d659+wYYOpV6+eeemll8zevXvNpEmTKrxUPDEx0bz//vvms88+M3fddZcjlx77M67p06eb6Oho8/bbb/tcWnzmzBljjDFnzpwxY8aMMRs3bjSHDh0yq1evNt26dTNt27Y158+fD9pxTZ482Xz44Yfmiy++MNu2bTNDhw41MTExZvfu3T5jD7X9ddGNN95osrKyLpkeDPvrzJkzZseOHWbHjh1Gkpk1a5bZsWOHOXz4sDHGmLFjx5phw4Z55794qfi//uu/mr1795p58+ZVeKl4Vb+ncEde/W/N5FXo7K+LyCtreVUnmjFjjJkzZ45p0aKFiY6ONunp6WbTpk3e1zIyMkx2drbP/EuWLDHt2rUz0dHRpmPHjmb58uU+r3s8HjNhwgSTnJxs3G63ufnmm83nn39ux1B8+DOutLQ0I+mSn0mTJhljjDl79qy59dZbTVJSkomKijJpaWnm4YcfduR/gv6M6+mnn/bOm5ycbAYMGGC2b9/us75Q3F/GGLNv3z4jyaxcufKSdQXD/lq7dm2F/6YujiM7O9tkZGRcskyXLl1MdHS0ad26tc99iC6q6vdUF5BX5FWo7S9jyKua5JXLGGP8O5YGAACAQAn7c8YAAACCGc0YAACAg2jGAAAAHEQzBgAA4CCaMQAAAAfRjAEAADiIZgwAAMBBNGMAAAAOohlDnTR8+HANGjTI6TIA4LLIq/BHM4agM3z4cLlcLrlcLkVFRalVq1Z65plndP78eadLAwAf5BUCoZ7TBQAV6devn3Jzc1VaWqpt27YpOztbLpdLL774otOlAYAP8go1xZExBCW3262UlBSlpqZq0KBByszM1KpVqyRJHo9H06ZNU6tWrRQbG6vOnTvr7bff9i5bXl6uBx980Pt6+/bt9dvf/tapoQAIc+QVaoojYwh6BQUF+uSTT5SWliZJmjZtmt544w29+uqratu2rdavX6/7779fSUlJysjIkMfjUfPmzbV06VI1btxYn3zyiR555BE1bdpUQ4YMcXg0AMIZeQUraMYQlP7jP/5DDRs2VFlZmYqLixUREaG5c+equLhYv/71r7V69Wr17NlTktS6dWt9/PHHmj9/vjIyMhQVFaXJkyd719WqVStt3LhRS5YsIdwABBx5hZqiGUNQ6tu3r37/+9/rhx9+0OzZs1WvXj3dc8892r17t86ePatbbrnFZ/6SkhJ17drV+3zevHlauHChjhw5onPnzqmkpERdunSxeRQA6gLyCjVFM4ag1KBBA7Vp00aStHDhQnXu3Fmvv/66rr32WknS8uXLddVVV/ks43a7JUmLFi3SmDFj9Jvf/EY9e/ZUXFycZs6cqc2bN9s7CAB1AnmFmqIZQ9CLiIjQuHHjlJOTo7///e9yu906cuSIMjIyKpx/w4YN6tWrlx577DHvtC+++MKucgHUYeQVrOBqSoSEwYMHKzIyUvPnz9eYMWM0atQo5efn64svvtD27ds1Z84c5efnS5Latm2rv/3tb/rwww/197//XRMmTNDWrVsdHgGAuoK8gr84MoaQUK9ePT3xxBOaMWOGDh06pKSkJE2bNk0HDx5UYmKiunXrpnHjxkmSHn30Ue3YsUNZWVlyuVy677779Nhjj+k///M/HR4FgLqAvIK/XMYY43QRAAAAdRUfUwIAADiIZgwAAMBBNGMAAAAOohkDAABwEM0YAACAg2jGAAAAHEQzBgAA4CCaMQAAAAfRjAEAADiIZgwAAMBBNGMAAAAO+v/8MRP0LgBCNgAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "markdown",
   "id": "75422875",
   "metadata": {},
   "source": [
    "More examples of dynamics analysis, for example, analyzing the fixed points in a recurrent neural network, please see [BrainPy Examples](https://brainpy-examples.readthedocs.io/). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e838d4a",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd4cedf",
   "metadata": {},
   "source": [
    "[1] Sussillo, D. , and  O. Barak . \"Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks.\" Neural computation 25.3(2013):626-649.\n",
    "\n",
    "[2] Si Wu, Kosuke Hamaguchi, and Shun-ichi Amari. “Dynamics and computation of continuous attractors.” Neural computation 20.4 (2008): 994-1025."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
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